Scientific Publications Directory

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Darwis, Herdianti; Puspitasari, Rahma; Purnawansyah; Astuti, Wistiani; Atmajaya, Dedy; Hasnawi, Mardiyyah, Faculty of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

This research investigates the use of deep learning for automatic waste classification, specifically using ResNet50 for feature extraction and combining it with various classification algorithms. The dataset comprises 1889 images categorized into four classes: plastic, metal, cardboard, and paper. Two approaches were evaluated: direct classification and feature extraction with ResNet50. The direct classification models, including SVM, KNN, and Random Forest, resulted in low performance, with an average accuracy of 60%. However, using ResNet50 for feature extraction significantly improved the classification accuracy across all models, with the combination of ResNet50 and SVM achieving an accuracy of 91%, and precision, recall, and F1-Score exceeding 92%. This demonstrates the effectiveness of ResNet50's feature extraction capability in enhancing the classification of images. The findings suggest that combining feature extraction and classification models provides a more accurate and efficient solution for automatic waste management systems, supporting the recycling process and waste management efficiency.

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Purnawansyah; Supriadi, Naufal Abiyyu; Manga, Abdul Rachman; Adawiyah, Rabiatul; Harlinda; Hasanuddin, Tasrif, Faculty of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

The volume of network traffic is ever-increasing, hence making DDoS attacks serious threats to the integrity of network security. This research paper focuses on improving the accuracy of detecting DDoS attacks using the ensemble machine learning methods of stacking, bagging, voting, and gradient boosting. In this work, is dataset big enough to correctly reflect real network traffic is used for training and testing purposes. The results indicate that gradient boosting has an accuracy rate of 0.99, followed by stacking and bagging, at 0.98. In this regard, both approaches tend to be efficient in the identification of the attack with limited errors in the prediction. Accordingly, the ensemble technique thus provides a robust approach toward the detection of DDoS attacks, while the study provides important information on the implementation of such strategies in real-time network security systems. Additional investigation is advised to examine supplementary datasets and optimize hyperparameters in order to enhance detection efficacy.

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Satra, Ramdan; Dahlan, Imram Afdillah; Darwis, Herdianti; Purnawansyah; Mujaddid, Syariful; Fattah, Farniwati, Faculty of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

This study focuses on comparing the performance of K-Nearest Neighbors (KNN), TabNet, and Wide & deep learning methods in classifying Distributed Denial of Service (DDoS) attacks on Software-Defined Networks (SDN). The use of SDN enables centralized control of network infrastructure, making it vulnerable to DDoS attacks occur due to the centralized nature of SDN architecture. Machine learning models, including KNN, TabNet, and Wide & deep learning, are applied to an SDN-specific DDoS dataset to evaluate their effectiveness in accurately classifying normal and malicious traffic. These models were tested using various data splits (60:40, 70:30, 80:20, and 90:10) to determine the optimal ratio for training and validation. KNN exhibited the highest accuracy, reaching 98% in both 80:20 and 90:10 splits, while wide & deep learning achieved 94.99% accuracy, and TabNet demonstrated a 93.59% accuracy. The results suggest that KNN, despite being a simpler algorithm, outperforms the more complex deep learning models in this specific task. The findings provide valuable insights for researchers and network administrators in selecting effective machine learning algorithms for DDoS detection in SDN environments.

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Darwis, Herdianti; Pagala, Arya Nanda Pratama; Anraeni, Siska; Amaliah, Tazkirah; As'ad, Ihwana; Tenripada, Andi Ulfah, Faculty of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Childfree is the choice to live without having or adopting children. This phenomenon is still considered a sensitive topic in Indonesia, where the prevailing belief holds that the primary goal of marriage is to have children. Numerous individuals share their perspectives on this matter through Twitter. In this research, 6654 raw data have been collected from Twitter using crawling techniques using Rapidminer and scraping using website scrape based on the keyword “childfree” which is preprocessed into clean data. The sentiment analysis model carried out includes categorizing childfree sentiment based on religious, medical and economic fields, then measuring the performance of the support vector machine, involving several methods such as RoBERTa labeling, bigram tokenizing, term frequency inverse document frequency weighting, 5 cross validation training, and synthetic minority over-sampling technique with edited nearest neighbors. The research results show that the economic category is the most influential field with 722 related sentiments, the accuracy performance of SVM gives a value of 75.95% on the linear kernel and the application of SMOTEENN gives a value of 95.94% on the linear kernel, it is proven that using SMOOTEENN can overcome data imbalance.

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Gaffar, Andi Widya Mufila; Azmi, Nurul; Alwi, Erick Irawadi; Abdullah, Syahrul Mubarak; Adawiyah, Rabiatul; Widyawati, Dewi, Faculty of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

MRI is a very important diagnostic tool in finding brain tumors, but recently the development of manual interpretation of MRI images has developed some challenges, such as diagnosis is often delayed, and there is a high chance of making mistakes. Recently, to hurry up the process, brain tumor detection has started applying machine learning methods. The classification of MRI images for brain tumors is done in this research paper by extracting their features with the ResNet-50 model. The classical machine learning algorithms that have been applied in the paper for classifying the tumors using the extracted features include Naive Bayes, Decision Tree, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest. In this respect, SVM and KNN have yielded the highest accuracy of 0.95 and 0.96 respectively, hence are the best methods in this task. The study's findings contribute to the development of quicker and more precise methods to help with brain tumor diagnosis in a medical context.

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Manga, Abdul Rachman; Nanda, As'syahrin, Department of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia; Handayani, Anik Nur; Herwanto, Heru Wahyu, Department of Electrical Engineering, Universitas Negeri Malang, Malang, Indonesia; Asmara, Rosa Andrie, Information Technology Department, State Polytechnic of Malang, Malang, Indonesia; Lantara, Dirgahayu, Faculty of Industrial Engineering, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Data classification plays a crucial role in artificial intelligence, particularly in enhancing model accuracy. This study focuses on classifying Toraja buffalo, a livestock breed with significant cultural importance in South Sulawesi, Indonesia. While the Single Input Approach is commonly used for classification, it often fails to capture all the necessary attributes to effectively distinguish between racial traits. Therefore, this research aims to evaluate the effectiveness of a multi-input approach, which integrates multiple data inputs to improve classification performance compared to the Single Input method. We employed four classification techniques: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Naïve Bayes, and Decision Tree, using both Single Input and Multi Input configurations. Model performance was assessed through Precision, Recall, F1 Score, and Accuracy metrics. The findings indicate that the Multi Input approach consistently outperformed the Single Input method. Notably, KNN achieved its best performance with Multi Input, recording an F1 Score of 0.7263 and an Accuracy of 0.7333, significantly surpassing the results obtained from Single Input. Similarly, SVM also demonstrated substantial performance enhancements with Multi Input. Overall, the study highlights the importance of incorporating a wider array of informative data to enhance the model's capability in accurately classifying specific categories, with KNN showing the most pronounced improvements

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Purnawansyah; Adnan, Adam; Darwis, Herdianti, Faculty of Computer Science, Universitas Muslim Indonesia, Jl. Urip Sumoharjo KM 5, Makassar, 90231, Indonesia; Wibawa, Aji Prasetya, Universitas Negeri Malang, Jl. Semarang No. 5, Malang, 65145, Indonesia; Widyaningtyas, Triyanna; Haviluddin, Universitas Mulawarman, Jl. Kuaro, Samarinda, 75119, Indonesia

Abstract

Facial Expression Recognition (FER) plays a crucial role in humancomputer interaction, yet improving its accuracy remains a significant challenge. This study aims to enhance the robustness and effectiveness of FER systems by integrating multiple machine learning techniques within a semi-supervised learning framework. The primary objective is to develop a more effective ensemble model that combines Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), Support Vector Classifier (SVC), and Random Forest classifiers, utilizing both labeled and unlabeled data. The research implements data augmentation and feature extraction techniques, utilizing advanced architectures such as VGG19, ResNet50, and InceptionV3 to improve the quality and representation of facial expression data. Evaluations were conducted across three dataset scenarios: original, feature-extracted, and augmented, using various labelto- unlabeled ratios. The results indicate that the ensemble model achieved a notable accuracy improvement of 87% on the augmented dataset compared to individual classifiers and other ensemble methods, demonstrating superior performance in handling occlusions and diverse data conditions. However, several limitations exist. The study's reliance on the JAFFE dataset may restrict its generalizability, as it may not cover the full range of facial expressions encountered in real-world scenarios. Additionally, the effect of label-to-unlabeled ratios on the model's performance requires further exploration. Computational efficiency and training time were also not evaluated, which are critical considerations for practical implementation. For future research, it is recommended to employ cross-validation methods for more robust performance evaluation, explore additional data augmentation techniques, optimize ensemble configurations, and address the computational efficiency of the model to better advance FER technologies.

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Herman; Putra Muhammad Dani Arya, Faculty of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia; Nasir, Haidawati, MIIT University Kuala Lumpur, Malaysia; Darwis, Herdianti; Mansyur, St. Hajrah, Faculty of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia; Noor, Megat Nurolazmi MM, MIIT University Kuala Lumpur, Malaysia

Abstract

This study focuses on being able to classify traditional Toraja carvings using Convolutional Neural Networks (CNN), focusing on three CNN architectures, specifically VGG-16, VGG-19, and a model that is concatenated from both. The aim is to determine the most effective architecture and ratio of training data and validation data sharing to achieve the highest classification accuracy. The image dataset consisting of seven different Toraja carving motifs or classes underwent data pre-processing, namely data augmentation, to improve model generalization and reduce overfitting. Experiments were conducted using four scenarios of training data and validation data separation. The final outcome of this research is that VGG-16 reached the best validation performance of 97.36% with a data 90%: 10% separation. It manifests its superior ability to Capture the information of complicated Toraja carving motifs. VGG-19 and the combined model also performed well, but the results were still below the best results of VGG-16 and emphasized that the VGG-16 architecture, especially with a data separation of 90%:10%, is the most reliable CNN architecture for accurately classifying Toraja carvings.

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Herman; Darwis, Herdianti; Nurfauziyah; Puspitasari, Rahma; Widyawati, Dewi; Faradibah, Amaliah, Faculty of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

This study aims to classify anxiety disorders in adolescents using three machine learning algorithms, namely naive bayes, C4.5, and K-Nearest Neighbor (K-NN). The data used was taken from the DASS-21 questionnaire, which contains 418 samples with 11 attributes, including age, gender, and seven anxiety-related questions. The algorithm was tested using the holdout technique with 80:20, 70:30, and 60:40 data splits, as well as the k-fold cross-validation technique. The results showed that the C4.5 algorithm performed best with 100% accuracy in the holdout technique, followed by naive bayes with 99% accuracy, and K-NN with 94% accuracy. In cross-validation testing, C4.5 also showed the highest accuracy of 98%, while naive bayes and K-NN achieved 89% and 92% respectively. This study concludes that the C4.5 algorithm is superior in classifying anxiety compared to naive bayes and K-NN, so it can be relied upon for machine learning-based diagnostic applications in supporting the detection of anxiety disorders efficiently.

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Azis, Huzain; Nasrullah, Departement of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia; Abdullah Munaisyah; Ismail, Suriana, Malaysian Institute of Information Technology, Universiti Kuala Lumpur, Kuala Lumpur, Malaysia; Purnawansyah; Syafie, Lukman, , Departement of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

The use of Unmanned Aerial Vehicles (UAVs) in aerial imaging is expanding rapidly, particularly in traffic monitoring and intelligent transportation systems. Detecting small and occluded vehicles in aerial images poses significant challenges due to varying resolutions and obstructions like buildings or trees. This study seeks to enhance vehicle detection accuracy by improving You Only Look Once (YOLO) models, with a focus on small and occluded object detection. Utilizing the COWC-M dataset and advanced data augmentation techniques such as Mosaic Augmentation, this research evaluates multiple YOLO variants. The YOLOv8-L model achieved the highest mAP50 score of 0.9899, demonstrating superior detection accuracy for small objects. Additionally, the YOLOv10-L model outperformed others with the best mAP50–95 score of 0.8715, indicating strong results across different intersection-over-union (IoU) ranges. Compared to YOLO-RTUAV, which achieved an mAP50 of 0.9353, the newer YOLO models provide significant improvements in both precision and recall. These findings contribute to the development of highly efficient, real-time vehicle detection systems suitable for large-scale surveillance applications in complex environments.

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Syafie, Lukman, Universiti Kuala Lumpur, 50250 Kuala Lumpur, Malaysia; Rismayanti, Universitas Negeri Malang, Kota Malang, Jawa Timur 65145, Indonesia

Abstract

Benign Prostatic Hyperplasia (BPH) is a prevalent non-cancerous enlargement of the prostate gland in aging men, often requiring early diagnosis to prevent urinary complications and improve patient outcomes. Traditional diagnostic procedures are limited by subjectivity and accessibility, especially in under-resourced regions. This study proposes an automated diagnostic approach using a deep learning model based on DenseNet121 to classify RGB prostate images into BPH and normal categories. A region-specific dataset consisting of 176 labeled RGB images, collected from a clinical facility in Bangladesh, was used to train and evaluate the model. Pre-processingincluded image resizing, normalization, and data augmentation to enhance generalization. Transfer learning was employed to fine-tune the model, which was trained over 10 epochs using the Adam optimizer and cross-entropy loss. The model achieved a best validation accuracy of 94.12%, with a recall of 72.2% for BPH detection, demonstrating its ability to identify pathological patterns in simple imaging modalities. Despite challenges such as dataset size and imbalance, the findings indicate that RGB image-based deep learning models can support clinical diagnosis of BPH in low-resource settings. This work contributes a lightweight, accessible solution for prostate disease screening and provides a foundation for future research on scalable AI-assisted diagnostics

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Harlinda; Rendi, Ahmad; Azis, Huzain; Indra, Dolly; Hayati, Lilis Nur; Kurniati, Nia, Faculty of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

The Cia-cia script, one of Indonesia's threatened cultural heritages, was chosen as the object of study due to the lack of research and documentation on the script. This study aims to create an automated system for classifying Cia-cia letters by utilizing the MobileNetV2 architecture and Convolutional Neural Networks (CNN). By applying deep learning techniques, the developed system achieves high accuracy, reaching 98.35% after 100 epochs. The research involved collecting 1823 handwriting samples covering 23 Ciacia alphabets, processed through a series of augmentation and normalization techniques to improve model performance. The results show that artificial intelligence-based technology is effective in documenting and preserving traditional scripts, while providing a foundation for the development of educational applications that can reintroduce Cia-cia alphabets to the younger generation. This research contributes to cultural preservation by integrating modern technology to ensure the continued use of the Cia-cia script in the digital era.

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Febriany, Mila, Departemen Kedokteran Gigi Anak, Fakultas Kedokteran Gigi, Universitas Muslim Indonesia, Makassar; Puspitasari, Yustisia, Departemen Ortodonsia, Fakultas Kedokteran Gigi, Universitas Muslim Indonesia, Makassar; Pamewa, Kurniaty, Departemen Kedokteran Gigi Anak, Fakultas Kedokteran Gigi, Universitas Muslim Indonesia, Makassar; Salim, Yulita, Fakultas Ilmu Komputer, Universitas Muslim Indonesia, Makassar

Abstract

Pemberian makan merupakan salah satu faktor predisposisi perkembangan Early Childhood Caries(ECC) yang umumnya terjadi pada usia anak prasekolah. Masa perkembangan anak mengalami peningkatan yang pesat pada usia 0-5 tahun, yang disebut fase “Golden Age”. Pada masa ini, kita dapat mendeteksi adanya kelainan tumbuh kembang anak yang meliputi aspek fisik, psikologi, dan sosial. Makanan memberikan nutrisi serta energi yang penting untuk kesehatan manusia. Korelasi antara zat gizi, makanan, dan pola makan memiliki implikasi terhadap pencegahan dan perkembangan penyakit kronis. Salah satu aspek psikologi yang dapat dipantau oleh orang tua adalah adanya gangguan perilaku makan. Diet dan nutrisi yang diberikan orang tua berpengaruh pada cara dan sikap orang tua terhadap pemberian makanan. Kesadaran tentang perilaku makan anak bermanifestasi pada karies anak usia dini dapat menjadi faktor penyebab gangguan pertumbuhan dan perkembangan anak di kemudian hari. Selain pola makan, teknik dan pembiasaan menyikat gigi turut berkontribusi dalam perkembangan karies anak. Metode pelaksanaan pengabdian ini dilakukan dengan tehnik penyuluhan langsung tentang perilaku makan menggunakan LCD diikuti pelatihan tehnik menyikat gigi. Kesimpulan pengabdian ini yakni pentingnya edukasi perilaku makan dan pelatihan teknik menyikat gigi yang berkasinambungan untuk ibu dan anak. Hasil yang didapatkan dari kegiatan pengabdian ini adalah peningkatan pengetahuan perilaku makan pada ibu, peningkatan pengetahuan mengenai teknik menyikat gigi pada balita dan anak-anak, pencegahan terjadinya ECC pada anak-anak dengan pemberian topical application fluor, bantuan sikat dan pasta gigi pada masyarakat, serta penyerahan media penyuluhan di kantor Desa Paddinging.

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As'ad, Ihwana, Program StudiSistem Informasi, Universitas Muslim Indonesia, Makassar, Indonesia; Salim, Yulita; Azis, Huzain, Program StudiTeknik Informatika, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Anak-anak dan remaja sering menggunakan media sosial dan bermain game secara berlebihan, yang dapat berdampak negatif terhadap kesehatan dan prestasi akademik. Kurangnya kesadaran akan keamanan digital dan etika penggunaan teknologi menjadikan mereka rentan terhadap ancaman siber. Kegiatan pengabdian ini bertujuan untuk meningkatkan literasi digital siswa Sekolah Kebangsaan Syeikh Mohd Idris Al-Marbawi di Malaysia melalui workshop interaktif. Metode pelaksanaan meliputi analisis kebutuhan, penyusunan dan pelaksanaan materi pelatihan, kepada 20 siswa kelas 5. Hasil kegiatan menunjukkan peningkatan pemahaman siswa terhadap penggunaan gadget yang bijak dan etika digital. Kegiatan ini juga mendorong pengembangan kemampuan interpersonal dan kesadaran terhadap keamanan siber.

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Manga, Abdul Rachman; Utami, Aulia Putri; Azis, Huzain; Salim, Yulita; Faradibah, Aulia, Department of Computer Engineering, Faculty of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

The surge in machine learning (ML) and artificial intelligence has revolutionized medical diagnosis, utilizing data from chest ct-scans, COVID-19, lung cancer, brain tumor, and alzheimer parkinson diseases. However, the intricate nature of medical data necessitates robust classification models. This study compares support vector machine (SVM), naïve Bayes, k-nearest neighbors (K-NN), artificial neural networks (ANN), and stochastic gradient descent on multi-class medical datasets, employing data collection, Canny image segmentation, hu moment feature extraction, and oversampling/under-sampling for data balancing. Classification algorithms are assessed via 5-fold cross-validation for accuracy, precision, recall, and F-measure. Results indicate variable model performance depending on datasets and sampling strategies. SVM, K-NN, ANN, and SGD demonstrate superior performance on specific datasets, achieving accuracies between 0.49 to 0.57. Conversely, naïve Bayes exhibits limitations, achieving precision levels of 0.46 to 0.47 on certain datasets. The efficacy of oversampling and under-sampling techniques in improving classification accuracy varies inconsistently. These findings aid medical practitioners and researchers in selecting suitable models for diagnostic applications.

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Bangsawan, Muhammad Hari; Salim, Yulita; Jabir, Sitti Rahmah, Program Studi Teknik Informatika, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Beberapa penelitian telah melakukan prediksi ketinggian gelombang laut menggunakan metode backpropagation, namun belum ada penelitian yang melakukannya di Selat Makassar. Oleh karena itu, penelitian ini bertujuan untuk menerapkan metode jaringan syaraf tiruan (JST) Backpropagation dalam memprediksi ketinggian gelombang laut di Selat Makassar. JST Backpropagation dipilih karena kemampuannya dalam menangani masalah prediksi dengan akurasi yang tinggi. Data yang digunakan dalam penelitian ini diperoleh dari Badan Meteorologi Klimatologi dan Geofisika (BMKG) Maritim Paotere Makassar, mencakup data harian tinggi gelombang, kecepatan angin, dan arah angin dari tahun 2019 hingga 2022. Data pelatihan mencakup periode 1 Januari 2020 hingga 30 Juni 2022, sedangkan data pengujian mencakup periode 1 Juli 2022 hingga 31 Desember 2022. Proses pelatihan menggunakan learning rate 0,1, 21 neuron pada lapisan input, 5 neuron pada lapisan tersembunyi, 7 neuron pada lapisan output, nilai batas error 0,01, beta 0,5, dan maxepoch 10.000. Hasil pengujian menunjukkan rata-rata MSE sebesar 0,1612 dan MAPE sebesar 28,27994%, menegaskan kemampuan model dalam memprediksi ketinggian gelombang laut dengan tingkat kesalahan yang dapat diterima.

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Sugiarti; Sugiarti, Irawati; Irawati, Atmajaya; Dedy, Program Studi Teknik Informatika, Fakultas Ilmu Komputer, Universitas Muslim Indonesia; Hayati; Lilis Nur, Program Studi Sistem Informasi, Fakultas Ilmu Komputer, Universitas Muslim Indonesia

Abstract

TPA Darul Ilmi, sebuah lembaga pendidikan nonformal yang berfokus pada pengajaran Al-Qur'an untuk anak-anak, menghadapi tantangan rendahnya minat dan perhatian dalam mempelajari huruf hijaiyah. Untuk mengatasi masalah ini, tim pengabdian masyarakat dari Universitas Muslim Indonesia menerapkan teknologi Augmented Reality (AR) sebagai solusi inovatif untuk membuat proses pembelajaran lebih interaktif dan menarik. Kegiatan ini dilakukan melalui beberapa tahap, yaitu persiapan dan perencanaan, sosialisasi dan pelatihan, implementasi, evaluasi, serta pemantauan. Hasilnya menunjukkan bahwa penggunaan aplikasi AR berhasil meningkatkan minat belajar anak-anak. Mereka lebih antusias dan tertarik mempelajari huruf hijaiyah dibandingkan metode konvensional. Interaksi yang lebih dinamis dengan konten pembelajaran membantu menjaga perhatian anak-anak dan memudahkan pengajar dalam menyampaikan materi. Secara keseluruhan, kegiatan ini memberikan dampak positif dengan memperkenalkan pendekatan teknologi modern dalam mendukung pembelajaran dasar Al-Qur'an, khususnya huruf hijaiyah, di TPA Darul Ilmi.

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Harlinda; Satra, Ramdan

Abstract

This research aims to provide recommendations for the placement of goods sold by the UMI Faculty of Computer Science mini supermarket. A data mining approach is used to determine the position of sales items between related items. This is done to make it easier for customers to search for items to buy based on the type of item. Another problem is determining the best-selling items and also determining the types of items that will receive promotions. The data mining approach uses association rules with a priori algorithms. Association rule mining is a data analysis technique used to find patterns and relationships in big data. This technique is widely used in business to help optimize marketing and sales strategies. The results of the rule association using an a priori algorithm show that if consumers buy 200 milli of Ultra Milk Slim Chocolate, they also buy 600 milli of LE MINERAL with a support value of 10% and confidence of 60%. This shows that these two items are related when consumers purchase.

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Pahendra, Muhammad Agung Maugi; Anraeni, Siska; Ilmawan, Lutfi Budi, Program Studi Teknik Informatika, Universitas Muslim Indonesia Jl. Urip Sumoharjo No.km.5, Makassar, 90231, Indonesia

Abstract

Traveloka, sebagai salah satu platform pemesanan perjalanan terkemuka, telah mencapai lebih dari 50 juta unduhan di Google Play Store. Pencapaian ini menunjukkan tingginya minat dan kepercayaan pengguna terhadap layanan yang ditawarkan. Namun, ulasan pengguna mengindikasikan adanya beberapa isu terkait performa dan kestabilan aplikasi yang perlu diperhatikan. Penelitian ini membandingkan performa metode Word Embedding Word to Vector (Word2vec) dan Embedding from Language Model (ELMo) menggunakan model Bidirectional Long Short Term Memory (BiLSTM) dalam analisis sentimen ulasan aplikasi Traveloka. Hasil penelitian menunjukkan bahwa model BiLSTM dengan Word2vec memiliki akurasi 76,13%, precision 75,22%, recall 77,99%, dan F1-measure 76,58%, lebih baik dibandingkan model dengan ELMo memiliki akurasi 74,38%, precision 70,49%, recall 78,77% dan F1-measure 74,40%. Model BiLSTM dengan Word2vec lebih efektif dalam analisis sentimen ulasan Traveloka, membantu mengidentifikasi dan menangani isu-isu pengguna guna meningkatkan kualitas layanan dan kepuasan pengguna.

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Troy, Muh Indra Endriartono Saputra; Jabir, Sitti Rahmah; Anraeni, Siska, Informatics Engineering, Faculty of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Lung cancer is one of the deadliest diseases in the world with a mortality rate of 25% of all cancer-related deaths in 2021. Lung cancer is a lung disease caused by genetic changes in respiratory epithelial cells, resulting in uncontrolled cell proliferation. In an effort to improve diagnosis and treatment, this study proposes an approach for multiclass performance evaluation using K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) algorithms based on 2024 data. in this study KNN is implemented conventionally while SVM applies 2 kernel processes, namely Linear and Polynominal. The data used is 1000 rows and uses 24 variables with a ratio of 70% training data and 30% testing data, the data in this study includes important information such as medical history, diagnostic test results, and clinical characteristics of patients. this study aims to determine which algorithm has the best performance by looking at the final results based on accuracy in identifying lung cancer data. Based on the research and discussion of SVM and KNN performance evaluation, the SVM algorithm produces an accuracy of 98.28%, surpassing the accuracy of the KNN algorithm of 97.25%. Therefore, the results show that the SVM algorithm is superior to the KNN algorithm. The KNN and SVM methods were implemented for multi-class classification of lung cancer, allowing identification of various subtypes of lung cancer with optimal accuracy.

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Darwis, Herdianti; Rahmasari, Putri Aulia; Irawati, Program Studi Teknik Informatika, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Sistem pakar yakni sebuah sistem yang diciptakan berdasarkan keahlian seorang pakar pada bidang terkhusus ke dalam sebuah program komputer. Penelitian ini membahas tentang Sistem Pakar Mendiagnosis Penyakit Gangguan Mental Dengan Metode Certainty Factor. Gangguan mental yakni sebuah keadaan kesehatan yang memengaruhi perasaan, pemikiran, perilaku, serta suasana hati atau gabungan diantaranya. Metode certainty factor dipakai sebagai nilai guna melakukan pengukuran taraf keyakinan penyakit gangguan mental. Penelitian ini bertujuan untuk menghasilkan aplikasi yang bisa memberi bantuan masyarakat dalam melakukan diagnosa dini pada gejala awal penyakit gangguan mental. Pada pengujian akurasi yang dilakukan menghasilkan nilai akurasi pada sistem yaitu sebesar 80% berdasarkan 10 sampel. Aplikasi sistem pakar melakukan diagnosis penyakit gangguan mental telah berhasil diimplementasikan ke dalam sistem memakai metode certainty factor guna mengambil kesimpulan berdasarkan pengetahuan pakar.

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Herman; Putri, An’nisa Pratama, Universitas Muslim Indonesia,Makassar, Sulawesi Selatan 90234,Indonesia; Noor, MegatNorulazmi Megat Mohamed, MIIT University Kuala Lumpur, 50250 Kuala Lumpur,Malaysia; Darwis, Herdianti; Hayati, Lilis Nur; Irawati; As'ad, Ihwana, Universitas Muslim Indonesia,Makassar, Sulawesi Selatan 90234,Indonesia

Abstract

Introduction: Passura', or Toraja carvings, are an essential element of the cultural heritage of the Toraja people in Indonesia. These carvings feature complex motifs rooted in nature, folklore, and spiritual symbolism. This study aims to evaluate the efficacy of four Convolutional Neural Network (CNN) architectures—VGG-16, DenseNet121, ResNet50V2, and EfficientNetB0—in classifying seven traditional Toraja carving motifs. Methods: A dataset of 700 images was collected and categorized into seven motif classes. The dataset was split into 80% for training and 20% for validation. Each CNN model was trained for 25 epochs with standard pre-processing, including resizing to 224×224 and normalization. Performance evaluation was conducted based on validation accuracy and confusion matrix analysis to assess classification precision and model overfitting. Results: EfficientNetB0 achieved the highest validation accuracy of 98%, although signs of overfitting were observed. ResNet50V2 followed closely with a validation accuracy of 95.33% and demonstrated the most balanced classification results across all motif categories. VGG-16 and DenseNet121 achieved 94.67% and 81.82%, respectively. Confusion matrix analysis confirmed the robustness of ResNet50V2 in correctly identifying complex patterns. Conclusions: The findings indicate that ResNet50V2 provides a reliable balance between accuracy and generalizability for classifying Toraja carvings, making it suitable for digital preservation of cultural heritage. EfficientNetB0, while achieving higher accuracy, may require additional regularization to avoid overfitting. This study contributes to the development of AI-driven cultural documentation and suggests future research with larger and more diverse datasets to improve model robustness

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Hayati, Lilis Nur; Randana, Fitrah Yusti; Darwis, Herdianti, Universitas Muslim Indonesia

Abstract

Machine learning-based sentiment analysis has become essential for understanding public perceptions of public services, including air transportation. Sultan Hasanuddin Airport, one of the main gateways in eastern Indonesia, faces the challenge of improving services amid changing user needs due to the COVID-19 pandemic. This study aims to compare the effectiveness of three machine learning algorithms- Support Vector Machine (SVM), Naive Bayes Multinomial, and K-Nearest Neighbor (KNN)-in analyzing the sentiment of user reviews related to airport services. The research also explores data splitting techniques, text preprocessing, data balancing using SMOTE, model validation, and method parameterization to ensure optimal results. The review data was retrieved from Google Maps (2021-2024) and underwent manual labelling. Text preprocessing includes normalization, stemming using Sastrawi, and stopword removal. The data-balancing technique uses SMOTE, while model evaluation is done with stratified k-fold cross-validation. SVM with a linear kernel showed the best performance, achieving an F1-score of 98.4%. Naive Bayes performed optimally, achieving an F1-score of 93.9%, while KNN recorded the best F1-score of 92.0%. SMOTE was shown to improve Naive Bayes' performance on unbalanced datasets, although it did not significantly impact SVM. The findings of this study provide data-driven recommendations to improve services at Sultan Hasanuddin Airport, such as the management of cleaning facilities, waiting room comfort, and passenger flow efficiency. In addition, this research opens up opportunities for developing real-time sentiment analysis systems that can be applied in other air transportation sectors.

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Syafie, Lukman; Azis, Huzain; Admojo, Fadhilah Tangguh, Universiti Kuala Lumpur, 50250 Kuala Lumpur, Malaysia

Abstract

Introduction: The preservation of Javanese script as part of Indonesia’s cultural heritage is increasingly urgent in the digital era, especially due to declining literacy among younger generations. This study aims to explore the effectiveness of YOLOv8, an advanced object detection algorithm, for recognizing handwritten Javanese numerals to support efforts in cultural digitization and education. Methods: A dataset of 2,790 handwritten Javanese numerals (0–9) was collected from 93 respondents. Each numeral was manually annotated using bounding boxes via the MakeSense.ai platform. The YOLOv8 model was trained using 80% of the data and validated on the remaining 20%. Training was performed in the PyTorch framework with data augmentation techniques to increase robustness. Model performance was evaluated using precision, recall, F1-score, and mean Average Precision (mAP), along with visualization through confidence curves and confusion matrices. Results: The model achieved a high validation precision of 88.3%, recall of 89.1%, and mAP of 0.88 at IoU 0.90. F1-score peaked at a confidence threshold of 0.89, while certain numerals like 'six' and 'nine' achieved near-perfect detection. Visualizations confirmed the model’s ability to accurately classify and localize characters in both training and unseen data. Minor misclassifications occurred between visually similar numerals. Conclusions: YOLOv8 demonstrates high effectiveness in recognizing handwritten Javanese numerals and holds significant potential for digital heritage preservation. Future work should focus on expanding the dataset, improving generalization under varied conditions, and integrating this model into educational tools and augmented reality applications for interactive learning.

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Nanda, Sultan Aziz Syaifullah, rogram Studi Teknik Informatika,Universitas Muslim Indonesia, Jalan Urip Sumoharjo, Makassar 90231, Indonesia; Azis, Huzain, Universiti Kuala Lumpur, 50250 Kuala Lumpur, Malaysia

Abstract

This research aims to design and develop a power control and monitoring device based on NodeMCU and the PZEM-004T module as the central controller and monitoring unit, enabling users to remotely control and monitor household lamp power consumption. The method used in this study is an experimental approach to evaluate the performance of the developed lamp power control and monitoring device. The NodeMCU ESP8266 microcontroller is employed to execute program instructions and enable remote control, while the PZEM-004T module functions as the power consumption meter. Through serial communication, it provides real-time data on power (Watt), energy consumption (kWh), and cost (IDR) to a smartphone via the Thinger.io web platform. Users can manage the ON-OFF status of home lighting and monitor power usage in real-time. The testing results indicate that the developed prototype successfully supports real-time control and monitoring with an average power measurement error of only 0.10%.

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Latip, Abdul, Institute of Advanced Informatics and Computing, Tasikmalaya 46115, Indonesia; Azis, Huzain, Malaysian Institute of Information Technology, Universiti Kuala Lumpur, Malaysia; Himawan, Hidayatulloh, Department of Informatics, UPN Veteran Yogyakarta, Indonesia, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia, Malaysia; Kurnia, Dian Ade, Department of Informatics Management, STMIK IKMI Cirebon, Indonesia, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia, Malaysia

Abstract

Cirrhosis is a chronic liver disease with significant health implications, responsible for 56,585 deaths annually, and ranking as the 9th leading cause of mortality worldwide. Early detection is crucial for effective treatment and better patient outcomes, as cirrhosis can progress to irreversible damage if not addressed in its initial stages. This research focuses on developing an advanced, integrated method for detecting cirrhosis by employing a combination of Synthetic Minority Over-sampling Technique (SMOTE) and machine learning models, specifically Gradient Boosting and XGBoost. The use of SMOTE is critical in this study as it addresses class imbalance in the dataset, which is a common challenge in medical diagnosis problems, especially when dealing with rare or minority conditions like cirrhosis. Class imbalance can lead to biased models that perform poorly on the minority class, which, in this case, could mean missing crucial cirrhosis diagnoses. SMOTE oversamples the minority class to ensure a more balanced dataset, which improves the model's ability to detect cirrhosis accurately. The research further includes a performance comparison between two powerful machine learning algorithms: Gradient Boosting and XGBoost. Gradient Boosting is known for its ability to optimize the model by focusing on misclassified instances in a sequential manner, while XGBoost, an advanced version of Gradient Boosting, is renowned for its speed and efficiency due to parallel processing and advanced regularization techniques.

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Markas, Muhammad Salman Al; Anraeni, Siska; Ilmawan, Lutfi Budiman, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Penggunaan internet dengan media sosial mempengaruhi masyarakat terhadap kegiatan yang dilakukan saat ini. Salah satu media sosial yang sekarang ini sedang populer digunakan oleh masyarakat adalah X. Informasi yang disebarkan dapat merupakan berita, opini, komentar, serta kritikan. Data yang didapat dari tweet ini dapat menjelaskan tanggapan masyarakat terhadap pelayanan pajak dari X. Maka dari itu penelitian ini sangat efisien jika X menjadi media untuk pengambilan data mengenai komentar Masyarakat sehingga dapat memberikan efektivitas perubahan yang diberikan kepada instansi pemerintah. Analisis sentimen menjadi proses yang sangat penting dalam memahami isi data dengan tujuan mengolah komentar yang diberikan oleh pengguna melalui tweet di X mengenai sebuah produk, layanan, dan instansi. Karya ilmiah ini bertujuan untuk membandingkan fitur Vector Bag Of Word dan TF IDF untuk mengevaluasi seberapa penting suatu term dalam sebuah dokumen pada dokumen yang lebih besar. Seperti diketahui bahwa komputer hanya mampu memproses input yang numerik sehingga data opini public berupa teks perlu direpresentasikan sebagai nilai numerik yang dikenal dengan ekstraksi fitur dan dapat dilakukan menggunakan model Binary Bag of Words (BOW), Count BOW dan Term Frequency-Inverse Document Frequency (TF-IDF) dikarenakan kedua teknik tersebut sangat berperan baik dan sama-sama digunakan untuk merepresentasikan numerik dari data teks serta memiliki kekurangan dan kelebihan masing masing. Berdasarkan hasil analisis maka dapat disimpulkan dengan menganalisis statement dengan menggunakan Bag Of Word dan TF-IDF dapat mengetahui jumlah tiap kemunculan kata di setiap kalimat dan dari hasil yang didapatkan bahwa kata yang sering diucapkan dalam sentimen yaitu dengan bobot nilai TF-IDF sebesar 0.1403.

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Asis, Muhammad Arfah; Ilmawan, Lutfi Budi; Alfiansyah, Nur Ikhwan; Ramadah, Rahman, Informatics Engineering, Faculty of Computer Science, Universitas Muslim Indonesia, Indonesia

Abstract

This study analyzes the performance comparison between eMMC and MicroSD storage in a single-node Hadoop environment, focusing on data processing efficiency using the Terasort and TestDFSIO benchmarks. In this experiment, four different data sizes, namely 500MB, 1GB, 1.5GB, and 2GB, were tested to evaluate how well each storage type handles data processing. The test results show that eMMC consistently outperforms MicroSD across all tested dataset sizes. The larger the data size processed, the more significant the performance comparison between the two storage types. At a data size of 2GB, eMMC is almost four times faster than MicroSD, showing a very clear advantage in processing efficiency. In addition, the results of the TestDFSIO test support this finding. In the test, eMMC shows a write speed that is 50% higher than MicroSD, and a read speed that is almost twice as fast at a data size of 10GB. This performance difference confirms that eMMC has a better capacity to handle large data, which is an important factor in applications that require intensive processing. The findings emphasize that eMMC offers better performance and stability than MicroSD, making it a more suitable choice for applications requiring high speed and efficiency in Hadoop environments. This research is expected to provide valuable insights for developers and researchers considering optimal storage solutions for big data processing.

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Ma’arif, A. Muh. Syafei Emil; Fattah; Farniwati, Darwis; Herdianti, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Dalam upaya untuk meningkatkan Keamanan jaringan komputer di Laboratorium Fakultas Ilmu Komputer UMI, seperti halnya serangan UDP Flooding dan SYN Flooding paket data yang datang sangat banyak dan menumpuk yang bisa saja terjadi kapan saja maka sangat dibutuhkan analisa. Serangan DOS adalah jenis serangan terhadap sebuah komputer atau server dengan cara menghabiskan sumber daya yang dimiliki sehingga tidak dapat berfungsi secara optimal. Sehingga secara tidak langsung menghalangi pengguna lain untuk memperoleh akses layanan dari komputer atau server tersebut. Penelitian ini melakukan klasifikasi serangan pada data-data yang diuji dengan menggunakan metode klasifikasi SVM (Support Vector Machines). Data yang diklasifikasi dari serangan DoS yaitu UDP Flooding dan SYN Flooding dengan mencatat aktivitas data traffic jaringan menggunakan tools Wireshark, Hasil penelitian ini Klasifikasi serangan dengan metode SVM menghasilkan tingkat akurasi yang sangat tinggi dalam waktu perekaman data selama 5 menit mendapatkan data record sebanyak 10.000 data yang sudah di seleksi untuk masing-masing serangan dengan rata-rata class yang diprediksi semuanya menghasilkan akurasi sebesar 100%

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Mude, Muh Aliyazid; Masyur, St Hajrah, Universitas Muslim Indonesia Makassar

Abstract

Keamanan komputer untuk jaringan internet penting karena tidak dapat dipisahkan dari bentuk serangan kejahatan siber yang bisa membahayakan data olehnya itu perlu berbagai upaya pengamanan dilakukan untuk mencegah terjadinya gangguan sistem keamanan IT berbasis website. Adanya kejahatan siber dapat membahayakan data penting yang tersimpan pada perangkat seperti pencurian kartu kredit, menyadap transmisi data, pemalsuan identitas, pemalsuan data, cyberstalking, penipuan, cyber spionase dan serangan siber lainnya. Karena itu perlu ada jaminan sistem keamanan aplikasi untuk memperbaiki sistem keamanan. Salah satu cara memperbaiki manajemen keamanan IT yakni mengikut standarisasi manajemen keamanan ISO 27001: 2005. Pada metode ini menggunakan model yang diterapkan yakni PDAC (plan, do, act, check) 4 model inilah yang akan digunakan untuk menilai sistem keamanan suatu sistem, sehingga perlu mendektesi aplikasi tersebut apakah memiliki sistem keamanan atau belum, karenanya tools untuk bisa mendeteksi suatu sistem/aplikasi sangat penting yang pada akhirnya memberikan rekomendasi arahan agar sesuai dengan standarisasi tersebut.

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Maghfirah, Rhima; Irawati; Irawati, Fattah; Farniwati, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Keunggulan kompetitif dalam industri food and beverage (F&B) tidak hanya bergantung pada kualitas produk, tetapi juga pada keunggulan pelayanan yang mampu memenuhi ekspektasi pelanggan. Cafe Choice Space, sebagai salah satu penyedia layanan kafe modern, menghadapi tantangan dalam mempertahankan kepuasan pelanggan akibat ketimpangan antara harapan pelanggan dan kenyataan layanan yang diberikan. Penelitian ini bertujuan untuk menganalisis tingkat kepuasan pelanggan menggunakan metode SERVQUAL (Service Quality Model) dengan mengukur lima dimensi utama: Tangible, Reliability, Responsiveness, Assurance, dan Empathy. Metode penelitian yang digunakan adalah kuantitatif deskriptif, dengan pengumpulan data melalui kuesioner yang diberikan kepada 30 responden, serta pengolahan data menggunakan Uji Validitas dan Uji Reliabilitas untuk memastikan keabsahan instrumen penelitian. Hasil penelitian menunjukkan bahwa terdapat gap signifikan pada dimensi Responsiveness (-16.67) dan Reliability (-12.00), menandakan bahwa pelanggan merasa pelayanan masih belum optimal dalam aspek kecepatan respons dan akurasi layanan. Kesimpulan dari penelitian ini menegaskan bahwa peningkatan kualitas layanan berbasis sistem digital dan pelatihan staf sangat diperlukan guna mengoptimalkan kepuasan pelanggan serta meningkatkan daya saing Cafe Choice Space di tengah persaingan industri F&B yang semakin kompetitif

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Sugarti; Gaffar, Andi Widya Mufila; Suallis; Samsul, Sri Wulandari, Universitas Muslim Indonesia, Indonesia

Abstract

Personal Computer (PC) atau komputer pribadi banyak dimanfaatkan untuk menyelesaikan berbagai macam permasalahan yang berkaitan dengan pengolahan data. Manfaat dan fungsi komputer yang begitu besar sehingga banyak orang yang menginginkan sebuah komputer yang bagus. Komputer dapat berfungsi dengan baik jika komponen yang ada di dalamnya memiliki spesifikasi yang sesuai. Komponen sistem komputer terdiri atas perangkat keras (hardware), perangkat lunak (software) dan orang atau pengguna (brainware). Komponen utama perangkat keras terdiri dari processor, motherboard, RAM, VGA, SSD, PSU dan perangkat pendukung lainnya. Kendala dalam membeli komponen komputer sering muncul karena kurangnya pengetahuan tentang spesifikasi optimal dan kompatibilitas antar komponen dan banyaknya pilihan sering menyebabkan hasil yang tidak sesuai harapan. Penelitian ini bertujuan membuat sistem rekomendasi pemilihan komponen komputer dengan menerapkan metode Item-Based Collaborative Filtering. Sistem dapat memberikan rekomendasi kepada pengguna tentang komponen hardware yang dapat dipilih sesuai kebutuhan. Hasil penelitian diperoleh bahwa metode Item-Based Collaborative Filtering dapat memberikan rekomendasi dengan melalui beberapa tahap yaitu pemberian rating, hitung rata-rata rating, menghitung nilai similarity kemudian menghitung nilai prediksi. Berdasarkan pemberian nilai rating pengguna dengan 6 komponen processor, diperoleh nilai rating prediksi 4 komponen E (AMD Processor Ryzen 5 3600Xt) dan nilai rating 3 komponen F (AMD Processor Ryzen 5 3500 Box 3.6Ghz 6 Cores Socket Am4). Sistem ini dapat dikembangkan dan bandingkan menggunakan metode user-based collaborative filtering sertadilakukan pengujian menggunakan Black Box agar dapat diketahui manfaat sistem dari sisi pengguna

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Syama, Firdayanti A.; Satra, Ramdan; Abdullah, Syahrul Mubarak, Program Studi Teknik Informatika, Fakultas Ilmu Komputer, Universitas Muslim Indonesia, Jl. Urip Sumoharjo km. 05, Makassar dan 90231, Indonesia

Abstract

This study aims to assess network performance on the first floor of Batara Guru Regional General Hospital and to determine whether the network meets users’ internet needs. The Quality of Service (QoS) analysis of the hospital’s Wireless LAN shows that throughput is of moderate quality during peak hours with an average of 518 kbps, and very good during off-peak hours with an average of 2,794 kbps. Packet loss is good at peak hours (4.1%) and very good off-peak (0.35%). For delay, network quality is very good in both peak and off-peak periods, averaging 60.7 ms and 75.05 ms, respectively. Jitter is good in both peak and off-peak periods, averaging 60.8 ms and 73.8 ms, respectively. The Quality of Experience (QoE) analysis of the hospital’s wireless network falls within the normal category, with an overall mean score of 3.55: Quality of Effectiveness 3.63 (normal), Quality of Efficiency 3.79 (normal), Usability 3.69 (normal), Expectations 3.26 (normal), and Context 3.41 (normal). In summary, the network at Batara Guru Regional General Hospital meets TIPHON and MOS standards and is categorized as good/satisfactory..

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Suherman, Muh Yeyen Dwi; Indra, Dolly; Manga, Abdul Rachman, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Kebiasaan merokok sering kita jumpai di kafe dan tempat umum lainnya. Kebiasaan ini dapat membuat pengunjung lain merasa canggung dengan asap rokok. Udara bersih tentu dibutuhkan oleh semua orang, baik orang dewasa, tua, maupun muda, Namun, banyak orang yang mengotori udara bersih, salah satu contohnya adalah dengan merokok di tempat umum. Kurangnya kesadaran dan pandangan negatif masyarakat setempat tentang risiko rokok bagi mereka dan orang di sekitar mereka, membuat kurangnya kapasitas masyarakat setempat mengubah cara berperilaku dan menciptakan lingkungan yang sehat. Oleh karena itu, upaya pencegahan harus dilakukan di rumah, di kelas, dan di masyarakat. Dengan hal tersebut peneliti membuat rancangan bangun prototype smoke detector berbasis arduino uno R3. Dengan menggunakan beberapa komponen alat yang dapat membantu mendeteksi asap rokok seperti arduino uno R3 berfungsi untuk mengolah hasil input-an yang dibaca oleh sensor MQ 135 kemudian mengeluarkan output sesuai dengan apa yang telah diprogramkan. MQ 135 berfungsi mendeteksi asap rokok, L298n sebagai pengatur kecepatan pada fan, LCD 12c menampilkan data yang tersedia, fan untuk mengeluarkan asap

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Anugrah, Rezky; Purnawansyah; Astuti, Wistiani,Teknik Informatika, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Universitas Muslim Indonesia telah berhasil menempati posisi ke-92 pemeringkatan WRWU. Keberhasilan atas pencapaian UMI didukung dengan adanya berbagai sarana dan prasarana salah satunya fasilitas perpustakaan. Pada perpustakaan Utsman Bin Affan buku diletakkan berdasarkan kategori buku yang telah diberikan penomoran yang disebut Dewey Decimal Classification (DDC). Namun, dalam penempatan buku belum diatur dengan melihat tingkat keseringan pengunjung dalam meminjam buku tersebut. Selain itu, pengunjung kesulitan dalam mencari kembali keberadaan buku yang sering dipinjam. Metode association rule khususnya algoritma apriori dapat digunakan dalam penataan koleksi buku di perpustakaan Utsman Bin Affan untuk mengidentifikasi asosiasi antara berbagai judul buku dengan menemukan support dan confidence yang menghasilkan pola asosiasi. Dari data transaksi peminjaman buku sejak 21 Januari 2022 sampai 19 januari 2024 adalah sebanyak 50 ID Mahasiswa perpustakaan dengan total 128 transaksi menghasilkan pola transaksi peminjaman mahasiswa UMI yakni Karya Umum, Ilmu-Ilmu Sosial dengan nilai support 14% dengan confidence 43%. Buku Filsafat dan Psikologi, Agama dengan nilai support 22% dengan confidence 36%. Selanjutnya buku Filsafat dan Psikologi maka mahasiswa juga akan meminjam ilmu-Ilmu Sosial dengan nilai support 22% dengan confidence 45%. Berdasarkan hasil tersebut disarankan untuk melakukan evaluasi reguler terhadap tata letak perpustakaan berdasarkan data penyimpanan dan umpan balik pengguna, dan melakukan penyesuaian yang diperlukan untuk meningkatkan efektifitasnya.

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Syaifullah, Sultan Aziz; Asis, Muhammad Arfah, Program Studi Teknik Informatika, Fakultas Ilmu Komputer, Universitas Muslim Indonesia, Jl.Urip Sumoharjo km.05, Makassar dan 90231, Indonesia

Abstract

This study aims to design a NodeMCU- and PZEM-004T-based device for power control and energy metering, serving as the central unit for control and monitoring, thereby enabling users to remotely control and monitor household lamp power consumption. An experimental method was employed to evaluate the performance of the developed controller and power/energy meter. The NodeMCU ESP8266 functions as the microcontroller to execute the device’s program and support remote control, while the PZEM-004T module performs lamp power measurements. Through wireless communication, the system delivers power (W), kilowatt-hours (kWh), and cost (IDR) information to a smartphone via the Thinger.io web interface. Users can set the lamp’s ON–OFF status and obtain real-time power measurements. Based on testing results, the prototype supports real-time control and monitoring with an average power-measurement error of 0.10%.

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Adhryanti, Nur; Gaffar, Andi Widya Mufila, Program Studi Teknik Informatika, Fakultas Ilmu Komputer, Universitas Muslim Indonesia

Abstract

Communication technology continues to evolve rapidly, one of which is the implementation of 4G LTE (Long Term Evolution) networks that offer high-speed internet and reliable performance. Based on preliminary data obtained through interviews conducted during this study, it was found that several areas in Marumpa Village still experience 4G signal quality that does not meet network standards. Therefore, signal strength measurements for 4G LTE in Marumpa Village need to be re-evaluated. This study aims to provide relevant information for the local community in Marumpa Village who utilize 4G LTE network services. The research was conducted using the Drive Test method with the G-NetTrack Pro application. The results indicate that the parameters RSRP, RSRQ, and SNR fall within good to excellent categories. Specifically, the RSRP value of -83.7 dBm at the Afiat Building is categorized as good, the RSRQ value of -8.5 dB at Cinranae Housing is classified as excellent, and the SNR value of 23 dB at the Afiat Building is also in the excellent category. On the other hand, poor performance for RSRP and RSRQ was recorded in the Babul Jannah area, with an RSRP value of -107.7 dBm and RSRQ value of -15.4 dB, both classified as poor. However, SNR values across all seven tested locations were found to be in the good to excellent categories, indicating stable signal-to-noise ratios overall

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Azis, Muhammad Arzhi; Irawati; Manga, Abdul Rachman, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Yayasan Kesehatan Telkom (YAKES Telkom) adalah suatu badan usaha nirlaba yang didirikan oleh PT Telekomunikasi Indonesia (Persero) Tbk. (Telkom) pada tanggal 1 April 1998. Yakes Telkom bergerak di bidang pelayanan kesehatan bagi karyawan, pensiunan, dan keluarga Telkom beserta anak perusahaannya. Yayasan ini terbagi di 7 kawasan. Salah satu kawasan daerah dari penelitian ini adalah Divisi Regional (DIVREG) VII KTI (Kawasan Timur Indonesia) yang berpusat di Makassar, Sulawesi Selatan. Penelitian ini bertujuan untuk menganalisis sebuah perancangan sistem informasi dalam pendaftaran pasien rawat jalan di TPKK (Titik Pelayanan Kesehatan Khusus) Pettarani, Makassar berdasarkan design user interface (UI) yang sudah responsif terhadap tampilan mobile sehingga dengan adanya sistem informasi seperti ini, karyawan maupun programmar dapat merasakan kemudahan dalam sistem pendaftaran pasien. Proses dalam penelitian ini menggunakan metode wawancara secara langsung dengan General Manager dan Officer IT. Perancangan maupun permodelan dilakukan dengan menggunakan sistem Flowchart, UML (Use Case Diagram, Activity Diagram, Sequence Diagram), maupun mockup dari aplikasi yang bernama Clinsys

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Bone, Syahrul; Hayati, Lilis Nur; Sugiarti, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

PT Rama Indonesia menghadapi tantangan dalam menentukan karyawan terbaik, terutama ditengah keberagaman karakteristik demografis yang dimiliki oleh tenaga kerjanya. Permasalahan yang sering muncul mencakup kesulitan dalam mempertahankan objektivitas selama proses penilaian, mengatasi bias yang tidak disadari, dan memastikan adanya kesempatan setara bagi semua karyawan untuk diakui dan berkembang. Penelitian ini bertujuan melakukan perancangan aplikasi penentuan pegawai terbaik menggunakan metode simple additive weighting. Hal ini merupakan salah satu solusi untuk mengatasi tantangan dalam menilai karyawan terbaik dengan menyediakan kerangka kerja yang terstruktur untuk evaluasi kinerja. Dengan menerapkan SAW, PT Rama Indonesia diharapkan dapat meningkatkan objektivitas dan efisiensi dalam proses penilaian kinerja, menciptakan lingkungan kerja yang lebih merata, dan meningkatkan kinerja perusahaan secara keseluruhan. Melibatkan kriteria seperti hadir terlambat tanpa ijin, disiplin waktu pulang, tidak masuk kerja tanpa ijin, tidak melakukan tugas, dikenai hukuman disiplin, pelaksanaan tugas pokok, kontribusi pada tugas tambahan, dan presisi laporan harian, SAW memberikan bobot relatif untuk masing-masing kriteria, memudahkan evaluasi kinerja karyawan secara holistic

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Indra, Dolly; Syahnur, Muh Haerdiansyah; Hayati, Lilis Nur, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

This study explores the implementation of a web-based information system for Karang Taruna Parangloe, a youth organization in Kelurahan Lanna, Parangloe District. The initiative was driven by the need to improve information dissemination, which previously relied on traditional methods like WhatsApp groups. While convenient, this approach had limitations in reaching a broader audience and effectively promoting organizational activities. To address these challenges, a Community Partnership Program (PKM) was carried out in four key stages: observation, socialization and training, technology implementation, and evaluation. During the observation phase, it became clear that the organization lacked an official platform for sharing updates and announcements. This was followed by socialization and training sessions, aimed at improving digital literacy among members, equipping them with the skills needed to manage and utilize a web-based system.The technology implementation phase focused on developing and launching the organization's official website, https://karangtarunaparangloe.com. Designed as a comprehensive information hub, the website includes a user guide to help administrators manage content effectively. The evaluation phase showed significant improvements in members' understanding and technical skills, with average post-test scores rising from 62 to 85. More than just a tool for sharing information, the website has helped enhance the organization's image and boost community engagement. This initiative demonstrates the power of digital transformation in empowering youth organizations and strengthening communication within communities. It also serves as an inspiring example for other organizations looking to adopt digital solutions to streamline operations and enhance their outreach efforts.

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Fattah, Farniwati; Sugiarti; Irawati, Program Studi Teknik Informatika, Universitas Muslim Indonesia, Jl. Urip Sumoharjo Km.5 Makassar; Wahab, Abdul; Nurul, Besse; Bora, Leni J; Munawir, Program Studi Pendidikan Agama Islam, Universitas Muslim Indonesia, Jl. Urip Sumoharjo Km.5 Makassar

Abstract

The digital transformation in education has encouraged the integration of technology-based media to create more interactive learning environments, especially at the early childhood education level. One of the main challenges faced by teachers in TK/TPA is delivering abstract materials such as hijaiyah letters in an engaging and comprehensible manner. The lack of varied teaching media has contributed to low student interest and participation in learning. Augmented Reality (AR) technology offers a promising solution by presenting learning materials in a more visual, concrete, and contextual format. This community engagement program aimed to enhance the understanding and awareness of TK/TPA Nurul Iman Pannara teachers regarding the use of AR as an interactive learning medium for hijaiyah instruction. The method involved preliminary observations, problem formulation, material preparation, and face-to-face implementation, including pre-tests and post-tests to evaluate participants’ comprehension levels. The results showed an increase in the average score from 3.15 (moderate understanding) to 4.01 (good understanding) after the activity. Teachers demonstrated high enthusiasm, actively participated in discussions, and expressed interest in further training for AR implementation. Positive responses also emerged regarding the potential of AR to improve the quality of instruction and student engagement. The documentation reflected strong involvement from participants and a collaborative learning atmosphere. In conclusion, the socialization activity effectively enhanced teachers’ digital literacy and opened opportunities for sustainable AR implementation in Islamic early childhood education settings.

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Hayati, Lilis Nur; Widari, Nyoman Sri; Bellanov, Agrienta, Jurusan Teknik Industri, Universitas Katolik Darma Cendika, Jl. Jl. Dr. Ir. H. Soekarno No.201 Surabaya

Abstract

Eco-enzyme is a fermentation product of organic materials, which is beneficial to the environment, but often suffers from defects in its production. This research aims to improve the quality of eco-enzymes using the Seven Tools and 5W+1H methods. Seven Tools identifies the causes of defects, while 5W+1H formulates improvement strategies. The analysis results show that color defects or black mold is the main problem with a percentage of 46.15%. Causal factors include poor quality raw materials, low labor skills, and suboptimal processing methods. Improvements were made by selecting fresh organic ingredients, 20% leafless vegetable composition, 30-minute soaking, and more optimal stirring. This strategy is expected to improve product quality and reduce the level of defects in eco-enzyme production.

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Sumardin, Andi, Hukum Keluarga, Fakultas Agama Islam, Universitas Muslim Indonesia; Abdullah, Syahrul Mubarak, Teknik Informatika, Fakultas Ilmu Komputer, Universitas Muslim Indonesia

Abstract

In Indonesia, the obligation for halal certification is regulated under Law Number 33 of 2014 concerning Halal Product Assurance. This regulation encourages food industry players and Micro and Small Enterprises (MSEs) in the food sector to obtain halal certification. This activity aims to enhance the knowledge and capabilities of MSE partners at SDIT Wahdah Islamiyah 01 Antang, Tompobulu District, Makassar City, in creating a Halal Product Assurance System (SJPH) manual for halal certification registration. The assistance was carried out over four months (August – November 2024). The Halal Certification Assistance program utilized the Self-Declare approach by supporting the development of the Halal Product Assurance System (SJPH) and the Digital Registration of SiHalal. This activity is expected to benefit the partners, as indicated by an increase in their knowledge of the halal certification process and their motivation to register for halal certification. The outcomes of this activity are An increase in understanding of the importance of Halal Certification for their products, The creation of a Manual Book as a guide for the Halal Product Certification process, and Enhanced knowledge among business owners about the procedure for halal certification registration through digitalization and obtaining Halal Certificates.

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Seniwati, International Relations Department, Social and Political Sciences Faculty, Hasanuddin University, Jl. Perintis Kemerdekaan No.KM.10, Kota Makassar, Sulawesi Selatan 90245, Indonesia; Amri, Khairul, Marine Science Department, Marine Science and Fishery Faculty, Hasanuddin University, Jl. Perintis Kemerdekaan No.KM.10, Kota Makassar, Sulawesi Selatan 90245, Indonesia; Rahmatia, Economic Department, Economic and Business Faculty, Hasanuddin University, Jl. Perintis Kemerdekaan No.KM.10, Kota Makassar, Sulawesi Selatan 90245, Indonesia; As'as, Ihwana, Computer Science Department, Computer Science Faculty, Indonesian Muslim University, Jl. inspeksi PAM lorong VI, Kota Makassar, Sulawesi Selatan 90234, Indonesia; Ranti, Munif Arif, International Relations Department, Social and Political Sciences Faculty, Hasanuddin University, Jl. Perintis Kemerdekaan No.KM.10, Kota Makassar, Sulawesi Selatan 90245, Indonesia; Angkananon, Chumphon, Political Science Department, Maejo University, 63, Nong Han, San Sai District, Chiang Mai 50290, Thailand

Abstract

Women have a significant role in supporting food security through women's empowerment programs in the tourism sector. Empowering women in the tourism sector can help improve the family economy. The training held in this activity aims to train and introduce information technology to local communities, especially women who are active as tourism entrepreneurs. The material presented includes information on how to manage business finances and digital literacy so that it can improve the quality of products sold. This training program is a form of women's empowerment to support food security and the achievement of SDGs 2030. Thirty people participated in this training activity. The results of the training activity showed that participants had a better understanding of how to process finances and use Android-based sales technology applications. This training activity includes three stages: 1) Introduction and training in business financial management and using Android in tourism businesses, 2) Mentoring, and 3) Practice. The implication of this training-based research is that tourism business actors can be more familiar with the use of applications that are utilized for tourism business activities. Tourism business actors, especially women, can have a significant impact on community economic growth which directly supports the achievement of SDGs 2030 through increased tourism business productivity.

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Purnawansyah; Ramadhan, Ahmad Mufli; Darwis, Herdianti; Lahuddin, Harlinda; Azis, Huzain; Tenripada, Andi Ulfa, Faculty of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Autism Spectrum Disorder (ASD) is fundamentally characterized by impairments in social communication and interaction, as outlined in the DSM-5. Leveraging speech utterances as a non-invasive modality, this study explores binary classification of ASD versus non-ASD based on transcribed utterances. We introduce the Makassar Autism Corpus, a newly constructed small-scale corpus containing utterances in Bahasa Indonesia with a regional Makassar dialect. To enrich the textual data, a set of linguistic features was extracted from each utterance, capturing structural and lexical attributes relevant to ASD indicators. Two classification models were implemented and evaluated: a traditional machine learning approach using Support Vector Machine (SVM) and a deep learning model based on the pretrained Transformer IndoBERT. Experimental results indicate that both models demonstrate the capacity to differentiate ASD from non-ASD utterances, with SVM showing greater stability on low-resource data. This research marks an early effort in applying computational linguistics for ASD classification in Indonesian, highlighting the viability of utterance-based analysis as an objective screening aid aligned with clinical diagnostic frameworks. Despite dataset “limitations, the findings underscore the potential of integrating linguistic feature analysis and natural language processing (NLP) to support early ASD identification in underrepresented language contexts.

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Syafie, Lukman; Purnawansyah; Herman; Awangga, Narendra; Wahyudi, Ifan, Fakultas Ilmu Komputer, Universitas Muslim Indonesia

Abstract

Program pengabdian ini bertujuan meningkatkan keterampilan digital siswa di Sekolah Kebangsaan Syeikh Mohd Idris Al-Marbawi, Malaysia. Masalah utama yang dihadapi sekolah ini adalah kurangnya akses siswa terhadap komputer dan aplikasi dasar seperti Microsoft Word. Melalui pelatihan intensif yang diberikan oleh Universitas Muslim Indonesia, siswa dibekali dengan keterampilan dasar penggunaan komputer dan aplikasi Microsoft Word. Pelatihan mencakup pengenalan perangkat keras dan perangkat lunak, serta praktik penggunaan fitur Microsoft Word, mulai dari dasar hingga fitur lanjutan seperti Word Art dan pengaturan kolom.Hasil dari pelatihan ini menunjukkan peningkatan signifikan pada keterampilan digital siswa sesudah pelatihan. Selain itu, sebagai luaran dari program ini, panduan pengantar komputer yang dapat digunakan oleh siswa secara berkelanjutan. Program ini berhasil meningkatkan literasi digital siswa dan diharapkan dapat menjadi model bagi sekolah-sekolah lain. Tantangan yang dihadapi adalah tingkat pemahaman siswa yang beragam, namun hal ini diatasi dengan sesi pendampingan dan konsultasi intensif.

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Darwis, Herdianti; Purnawansyah; Umalekhoa, Alfi Syahrin; Adnan, Adam; Umar, Fitriyani; AR, Muh. Aqil Fajar, Universitas Muslim Indonesia, Jl. Urip Sumoharjo KM 5, 90231, Makassar, Indonesia; Salim, Yulita, Universiti Kuala Lumpur, 1016 Jl. Sultan Ismail, Bandar Wawasan, 50250, Kuala Lumpur, Malaysia; Raja, Roesman Ridwan, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, 820-8502, Fukuoka, Japan

Abstract

This research investigates the application of clustering techniques to network congestion data at Universitas Muslim Indonesia, employing a hybrid metric approach based on packet loss and delay. The study utilized two algorithms, K-Means and K-Medoid, applied in a semi-supervised scenario to group 255,147 network data points into 3, 4, and 5 clusters, considering 10 principal variables. During the pre-processing phase, data cleansing was conducted to address missing values, followed by normalization to standardize the scale of numerical variables, thereby preparing the data for the clustering process. Model validation was performed using four cluster evaluation methods: Gap Statistic, Davies-Bouldin Index, and Elbow Method. The evaluation results indicate that both algorithms were capable of forming valid and reliable clusters. However, the K-Means algorithm demonstrated superior performance compared to K-Medoid, particularly when utilizing three Quality of Service variables: throughput, packet loss, and delay. In this configuration, K-Means yielded more stable clusters, a clearer separation between clusters, and a more structured visualization. Consequently, K-Means is considered more optimal for classifying network congestion levels and presents an effective approach for network data segmentation

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S, Muhammad Ikhsan.; Hayati, Lilis Nur; Widyawati, Dewi, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Berbagai industri termasuk ritel telah berubah sebagai akibat dari kemajuan teknologi informasi. Alfagift sebuah aplikasi yang dikembangkan oleh Alfamart untuk membantu pelanggannya berbelanja secara digital dan salah satu platform yang tengah berkembang pesat. Tujuan penelitian ini adalah untuk melakukan analisis sentimen masyarakat terhadap sistem perbelanjaan Alfagift dengan menggunakan metode Naive Bayes. Metode Naïve Bayes ini dipilih karena kemampuannya untuk mengklasifikasikan teks berdasarkan probabilitas, yang memungkinkan untuk menghasilkan hasil yang akurat dengan data yang relatif kecil. Data yang digunakan untuk penelitian ini adalah komentar atau ulasan pengguna tentang aplikasi Alfagift yang diambil dari platform review aplikasi. Analisis sentimen mencakup pengumpulan data, Preprocessing, serta penerapan Naive Bayes untuk membagi sentimen menjadi dua kategori utama yaitu positif dan negatif. Penelitian ini mengumpulkan 6.650 data ulasan pengguna dan didapatkan 4.937 kelas sentimen positif dan 1.713 kelas sentimen negatif. Naïve Bayes Classifier dalam mengklasifikasi data mendapatkan akurasi tertinggi saat pembagian data latih dan data uji dengan perbandingan sebesar 90:10 memperoleh Accuracy 85.94%, recall 81.81%, Precision 91.83% dan F1-Score sebesar 86.52%. Hasil dari analisis ini menunjukkan bagaimana respon masyarakat terhadap fitur-fitur yang ada dalam aplikasi, serta memberikan wawasan tentang kekuatan dan kelemahan dari sistem perbelanjaan Alfagift. Penelitian ini juga membuktikan efektivitas metode Naive Bayes dalam menganalisis sentimen secara otomatis dan memberikan insight yang berbasis data

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Ningsih, Sulistia; Satra, Ramdan; Gaffar, Andi Widya Mufila, Universitas Muslim Indonesia

Abstract

Di era digital, akses terhadap internet dan jaringan lokal nirkabel (Wireless LAN) menjadi sangat penting, khususnya di lingkungan akademik, salah satunya di Fakultas Agama Universitas Muslim Indonesia.Tujuan penelitian iniuntuk mengetahui area blank spotatau sinyal yang lemah berdasarkan jaringan Wireless LANdan memberikan rekomendasi penempatan Access Point. Penelitian ini menggunakan metode jenis pendekatan eksperimen, dengan menggunakan metode lapangan yang melibatkan pengumpulan data melalui observasi. Hasil penelitian ditunjukkan dengantiga hasil simulasi access pointdengan menggunakan parameter RSL (Received Signal Level) SIR (Signal to Interferance Ratio) yang telah diimpelementasikan dengan menggunakan 8 access point(AP), dimana masing-masing 4AP pada lantai 2 dan 3 Fakultas Agam UMI menggunakan aplikasi NetSpot.Adapun hasil dari nilai RSL untuk simulasi Apada lantai 2dan 3 adalah -41,02 dBm dan -41,20 dBm, simulasi B pada lantai 2 dan 3 adalah -41,07 dBm dan -42,95 dBm, simulasi C pada lantai 2 dan 3 adalah -41,35 dBm dan -43,02 dBm. Sedangkan hasil SIR untuk simulasi A pada lantai 2 dan 3 adalah 47,62 dB dan 46,37 dB, simulasi B pada lantai 2 dan 3 adalah 48,27 dB dan 47,55 dB, simulasi C pada lantai 2 dan 3 adalah 48,87 dB dan 47,95 dB. Dari hasil penelitian maka dapat disimpulkan bahwa simulasi C memberikan hasil optimisasi terbaik dengan presentasi coverage areaterbaik. Hasil rata-rata RSL dan SIR termasuk dalam kategori kekuatan sinyal “Very Good” (Sangat Bagus), yang menunjukkan bahwa penempatan Access Pointpada simulasi C mampu memberikan performa jaringan yang sangat memadai di kedua lantai tersebut.

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Faradibah, Amaliah; Widyawati, Dewi; Belluano, Poetri Lestari Lokapitasari; Tenripada, A. Ulfah; Utami, Aulia Putri; Fatahillah, Furqon; Tuasamu, Fatimah A.R.

Abstract

The use of Information Technology has penetrated various sectors, including education. In the digital era, video editing applications are among the most widely used tools, as students tend to be more engaged with video-based content than with text-based learning materials. This study aims to improve teaching methods by empowering teachers to create engaging learning videos using digital applications. The program was implemented at SDN 133 Inpres Paririsi Takalar, involving 27 elementary school teachers with diverse educational backgrounds, teaching subjects, and teaching experience. A Practical and Participatory training model was applied over a two-month period. Evaluations were conducted before and after the training to assess its effectiveness in enhancing teachers’ video editing skills. The results showed a significant improvement in teachers' abilities to use video editing applications and increased confidence in delivering digital-based learning content. The main challenges faced by teachers included limited initial digital literacy and time constraints during implementation. This study contributes to addressing the digital content creation skill gap among elementary school teachers. The expected outputs include: (1) a user guide module for video editing applications, (2) publication in an accredited national journal, and (3) enrichment materials for the Multimedia Systems course. Further research is recommended to explore the long-term impact of the training and its applicability to other educational levels.

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Rahmatullah, Muhammad Faiq; Belluano, Poetri Lestari Lokapitasari; Darwis, Herdianti, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Google Play Store adalah salah satu platform distribusi aplikasi terbesar yang memungkinkan pengguna memberikan ulasan terhadap aplikasi yang mereka pakai. Di era digital saat ini, ulasan pengguna menjadi sumber data penting untuk menilai performa dan kualitas aplikasi. Namun, banyaknya jumlah ulasan membuat analisis secara manual menjadi kurang efisien. Oleh karena itu, peracangan ini ini mengadopsi pendekatan machine learning untuk mengklasifikasikan ulasan ke dalam kategori sentimen positif, negatif, atau netral. Proses analisis meliputi beberapa tahap, seperti pengumpulan data, praproses teks, ekstraksi fitur dengan TF-IDF, pelatihan model menggunakan Random Forest, serta evaluasi kinerja model. Hasil evaluasi menunjukkan bahwa model yang dikembangkan berhasil mengklasifikasikan sentimen dengan akurasi sebesar 68.5%, dengan performa terbaik pada sentimen negatif. Selain itu, penerapan metode Random Forest juga membuka peluang untuk pengembangan sistem analitik otomatis yang dapat digunakan oleh pengembang aplikasi dalam meningkatkan kualitas layanan mereka. Dengan memahami kecenderungan opini pengguna secara cepat dan akurat, pengambilan keputusan dalam pengembangan fitur baru atau perbaikan bug dapat dilakukan secara lebih terarah. Implementasi metode ini juga berpotensi untuk diterapkan pada sektor lain seperti e-commerce, layanan publik, atau media sosial, di mana opini pengguna menjadi salah satu aspek penting dalam evaluasi layanan

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Belluano, Poetri Lestari Lokapitasari, Department of Electrical Engineering and Informatics, Universitas Negeri Malang, Indonesia; Department of Informatics Engineering, Universitas Muslim Indonesia, Makassar, Indonesia; Patmanthara, Syaad; Ashar, Muhammad, Department of Electrical Engineering and Informatics, Universitas Negeri Malang, Indonesia; Kurniawan, Fachrul, Department of Informatics Engineering, Universitas Islam Negeri Maulana Malik Ibrahim, Malang, Indonesia; Kurubacak, Gulsun, Department of Distance Education, Anadolu University, College of Open Education, Eskisehir 26470, Turkey

Abstract

This study introduces a clustering-driven adaptive User Experience (UX) architecture for e-learning systems, aligning machine learning segmentation with the 21st-century 4C educational framework (critical thinking, communication, collaboration, creativity). The objective is to dynamically personalize digital learning interactions through a microservices architecture responsive to users' UX profiles. A quantitative survey was conducted involving 50 active users of Shopee and Tokopedia, whose interaction feedback was mapped using the User Experience Questionnaire (UEQ). Three unsupervised clustering techniques—KMeans, Agglomerative, and DBSCAN—were compared. KMeans outperformed the others with a silhouette score of 0.157, compared to 0.146 for Agglomerative and −0.017 for DBSCAN, identifying three meaningful clusters representing high, medium, and low UX proficiency. A one-way ANOVA test confirmed statistically significant differences (p < 0.01) among the clusters in dimensions such as error clarity, support responsiveness, and user confidence. These UX profiles were then mapped to individualized microservices: Cluster 0 received autonomous content with minimal support, Cluster 1 was offered guided prompts, and Cluster 2 was provided with simplified interfaces and proactive assistance. Each cluster was aligned with specific 4C competencies to ensure pedagogical relevance. The proposed architecture, built with gRPC-based microservices, enabled asynchronous, low-latency personalization based on user cluster membership. The novelty of this research lies in its dual alignment—technological (microservices + machine learning) and educational (4C competency mapping)—to construct a scalable and responsive e-learning environment. The system design, although validated through simulation, demonstrates a practical foundation for future deployment in platforms like Moodle or OpenEdX. By linking behavioral UX clustering to pedagogical intervention strategies, this study offers a model for adaptive, data-informed instructional systems that are both scalable and learner-centered.

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Fauzi, Muh Hilmy Noor; Belluano, Poetri Lestari Lokapitasari; Hasnawi, Mardiyyah, Universitas Muslim Indonesia, Makassar

Abstract

Penelitian ini bertujuan mengembangkan teknik pemodelan desain aplikasi mobile untuk memantau pengiriman sampah daur ulang dengan menggabungkan dua metode yaitu Rapid Prototyping dan User-Centered Design (UCD). Aplikasi yang dikembangkan untuk meningkatkan efektivitas pengiriman sampah daur ulang serta memastikan keterlibatan pengguna dalam proses pengelolaan limbah. Pemodelan aplikasi mobile dengan pendekatan Rapid Prototyping dalam pembuatan prototipe interaktif secara cepat sementara User Centre Design diterapkan dalam hal pemahaman secara mendalam terhadap kebutuhan pengguna. Kombinasi Teknik pemodelan antara Rapid Prototyping dan User Centre Design menghasilkan prototipe aplikasi yang baik dalam memonitor pengiriman sampah daur ulang, memberikan kontribusi positif dalam mengatasi permasalahan monitoring pengiriman sampah dan dampak positif pada industri daur ulang dimana pengujian akhir prototipe Efficiency, Effectiveness, Satisfaction, dan Usability yang telah dikembangkan berhasil menunjukkan hasil yang sangat positif. Proses pengembangan prototipe aplikasi mobile melalui beberapa tahapan antara lain analisis awal, perancangan prototipe, verifikasi dan pengujian awal, iterasi dan perbaikan, serta evaluasi akhir desain. Hasil Penelitian ini menunjukkan bahwa tingkat Efficiency dan Effectiveness pada pengujian akhir prototipe aplikasi sangat tinggi, dengan rata-rata nilai 5.00 sedangkan dari sisi Satisfaction dan Usability memperoleh rata-rata nilai sebesar 4.75.

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B, Poetri Lestari Lokapitasari; Patmanthara, Syaad, Department of Electrical Engineering and Informatics, Faculty of Engineering, State University of Malang, Malang, Indonesia; Ashar, Muhammad, Department of Informatics Engineering, Faculty of Science and Technology, Maulana Malik Ibrahim State Islamic University, Malang, Indonesia; Kurniawan, Fachrul, Department of Informatics Engineering, Faculty of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia.

Abstract

This paper evaluates the user experience of a microservices-based e-learning platform created for technopreneurship education using the User Experience Questionnaire (UEQ). Microservices present new chances to improve system performance, dependability, and learner involvement when educational systems choose modular and scalable designs. The study presents a benchmarking strategy by contrasting the newly created platform with two extensively utilized commercial platforms, Shopee and Tokopedia, which both use scale microservices. Fifty undergraduates participated in the study and evaluated six fundamental UX dimensions: attractiveness, perspicuity, efficiency, dependability, stimulus, and novelty. Quantitative research shows that the e-learning system works well in terms of pragmatic quality (clarity, efficiency, and reliability) and hedonic quality (stimulus and creativity). Comparatively, in perspicuity and efficiency, T-test comparisons reveal statistically significant benefits of the e-learning platform over Tokopedia; similarly, in stimulation and novelty, over Shopee. These findings imply that the microservices-based design improves emotional involvement and perceived innovation in the learning environment and supports functional performance. The study indicates that tools usually used in commercial environments allow one to assess user experience in education effectively. It also emphasizes how the design of learner-centred digital platforms can be guided by benchmarking against industry systems. The results provide helpful information for teachers trying to match educational technologies with user expectations moulded by actual digital experiences and for e-learning developers.

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Manga, Abdul Rachman; Atmajaya, Dedy; Faradibah, Amaliah, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Agriculture utilizes biological resources to produce food, industrial raw materials, energy sources, and manage the environment. This sector plays a strategic role in national economic development. This research aims to design an automatic spraying system for shallot plants based on soil moisture using soil moisture sensors. This system utilizes soil moisture sensors to detect the water content in the soil as well as soil moisture sensors to measure the air humidity around the plants. Data from both sensors are processed by the microcontroller to regulate the timing and duration of the spraying. The prototype of this system was built using soil moisture sensors, soil moisture sensors, microcontrollers, water pumps, solenoid valves, and other supporting components. Testing was conducted in the field with red onion plants as the test subjects. The results show that the system is capable of functioning effectively in watering plants based on soil moisture levels. The sensor works accurately in measuring water content, while the microcontroller successfully controls the spraying optimally. The implementation of this system has proven to increase water usage efficiency and support better growth of red onion plants. Thus, this automatic spraying system offers an environmentally friendly and efficient solution for irrigation based on soil moisture and soil moisture sensors.

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Kurniati, Nia; Hasnawi, Mardiyyah; Abdullah, Syahrul Mubarak; Hasanuddin, Tasrif, Program Studi Teknik Informatika, Universitas Muslim Indonesia, Makassar, Indonesia; Puspitasari, Andi, Program Studi Pendidikan Bahasa & Sastra Indonesia, Universitas Muslim Indonesia, Makassar,Indonesia.

Abstract

Kegiatan Pengabdian kepada Masyarakat (PkM) yang berjudul Transformasi Literasi Ilmiah bagi Guru di SDIT Wahdah Islamiyah 01 Makassar melalui Pelatihan Menulis Berbasis Teknologi Digital dilaksanakan untuk menjawab kebutuhan peningkatan kompetensi guru dalam literasi ilmiah dan pemanfaatan teknologi digital dalam pembelajaran. Perkembangan teknologi menuntut guru tidak hanya mampu menulis secara ilmiah, tetapi juga memahami dan memanfaatkan berbagai platform digital yang menunjang proses penulisan, kolaborasi, serta pengelolaan dokumen. Namun, sebagian guru masih memiliki keterbatasan dalam penggunaan Google Tools seperti Google Docs, Google Scholar, Google Drive, dan Google Classroom, sehingga proses pembelajaran dan administrasi belum berjalan secara optimal. Pelatihan ini dirancang dengan metode observasi, wawancara, studi pustaka, pelatihan teknis, pendampingan langsung, serta evaluasi melalui penyebaran kuesioner. Materi pelatihan mencakup teknik penulisan ilmiah, pengolahan dokumen digital, pencarian referensi ilmiah, penyimpanan dan berbagi dokumen secara kolaboratif, serta pemanfaatan platform digital untuk pembelajaran. Pelatihan berlangsung interaktif dengan praktik langsung yang dibimbing oleh dosen dan mahasiswa pendamping. Hasil kegiatan menunjukkan peningkatan signifikan pada pemahaman dan keterampilan guru dalam menulis ilmiah berbasis teknologi digital. Guru mampu menghasilkan draft artikel ilmiah, mengelola dokumen secara daring, dan memanfaatkan Google Tools dalam pembelajaran. Respon kuesioner menunjukkan bahwa peserta merasa pelatihan bermanfaat, relevan, dan mudah diterapkan dalam tugas sehari-hari. Meskipun terdapat kendala seperti perbedaan kemampuan digital dan gangguan internet, kegiatan tetap berjalan efektif. Secara keseluruhan, PkM ini berhasil mendorong transformasi literasi ilmiah dan kompetensi digital guru, serta menjadi langkah penting bagi sekolah dalam mewujudkan pembelajaran modern yang kreatif, kolaboratif, dan berbasis teknologi.

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Syah, Fathurrahman Putra; Hasanuddin, Tasrif; Kurniati, Nia, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Perkembangan media sosial, khususnya Twitter, telah menjadi sarana utama bagi masyarakat Indonesia dalam menyampaikan opini dan pandangan terhadap berbagai isu, termasuk politik. Analisis sentimen menjadi metode yang efektif untuk memahami kecenderungan opini publik melalui media sosial. Penelitian ini bertujuan untuk menganalisis sentimen tweet netizen terkait isu politik di Indonesia dengan menerapkan algoritma Naïve Bayes Classifier. Data dikumpulkan dari tweet yang membahas isu politik dan diproses melalui tahap preprocessing, seperti pembersihan data, normalisasi kata, serta penghapusan kata yang tidak relevan. Hasil penelitian menunjukkan bahwa model Naïve Bayes memiliki akurasi sebesar 72% setelah dilakukan optimasi, dengan kinerja yang baik dalam mendeteksi sentimen positif dan netral, namun masih menghadapi tantangan dalam mengidentifikasi sentimen negatif akibat ketidakseimbangan data. Sentimen negatif umumnya berisi kritik terhadap kebijakan pemerintah, sementara sentimen positif mencerminkan harapan dan dukungan terhadap proses politik. Kelemahan dalam penelitian ini disebabkan oleh asumsi independensi fitur dalam Naïve Bayes serta kompleksitas bahasa dalam tweet, seperti sarkasme dan konteks yang ambigu. Oleh karena itu, penelitian lebih lanjut disarankan untuk mengembangkan pendekatan yang lebih canggih, seperti deep learning atau model hibrida, guna meningkatkan akurasi klasifikasi sentimen politik di media sosial

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Septiani, Berlian; Hasanuddin, Tasrif; Astuti, Wistiani, Universitas Muslim Indonesia, Makassar, 90231, Indonesia

Abstract

Lontara script is a traditional writing system of the Bugis-Makassar people in South Sulawesi, used to write the Bugis, Makassar, and Mandar languages. This system is based on an abugida, in which each letter represents a consonant with an inherent vowel. It was once used to record history, customary law, and literature, but its use has declined due to the influence of the Latin alphabet. Today, the Lontara script is preserved through education and digitization as part of the cultural heritage of the Indonesian archipelago. In this article, the researchers attempt to use a dataset of handwritten Lontara Bugis-Makassar characters. The process begins with the collection of character datasets, which are then processed through Canny segmentation and Hu Moment feature extraction to obtain a representation of the shape that is invariant to rotation and scale. The processed data was divided into training and testing data, then classified using the K-NN, Decision Tree, and Random Forest algorithms. The results showed that the KNN algorithm with 6 neighbors achieved the highest accuracy, precision, and recall of 98%. The Decision Tree algorithm achieved an accuracy of 96.67%, precision of 96.22%, recall of 95.33%, and an F1-score of 95.98%. Meanwhile, Random Forest showed an accuracy of 96.67%, precision of 96.34%, recall of 96%, and an F1-score of 95.98%.

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Abstract

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Rahman, Agussalim; Sadariah, Sadariah; Rasyid, Abd; Kurniaty, Kurniaty, Manajemen, STIE Tri Dharma Nusantara, Makassar; Satra, Ramdan, Teknik Komputer, Universitas Muslim Indonesia, Makassar

Abstract

UPZ Masjid Babul Firdaus adalah sebagaimana UPZ dan pengelola zakat lainnya menghadapi tingkat pengumpulan dan pendistribusi zakat yang belum optimal. Tujuan dari pelaksanaan kegiatan pengabdian kepada masyarakat ini adalah untuk memberdayakan UPZ Masjid Babul Firdaus sebagai lembaga pengelola zakat di kelurahan Batangkaluku agar dapat berperan dalam mengelola zakat melalui pembuatan Sistem Informasi Zakat supaya UPZ dapat melakukan pengumpulan dari masyarakat yang wajib zakat (muzakki) dan menyalurkannya kepada masyarakat yang berhak menerima zakat (mustahik) terutama dalam pengentasan kemiskinan. Pembuatan Sistem Informasi Zakat telah melewati serangkaian tahapan kegiatan yaitu: 1) Sosialisasi kepada pemerintah setempat, 2) sosialisasi dan edukasi zakat kepada masyarakat, 3) pelatihan dan pembekalan kepada Tim Konsultan Zakat, 4) pendataan dan pemetaan status zakat masyarakat sebagai muzakki, munfiq atau mustahik, 5) penerapan teknologi melalui pembuatan Sistem Informasi Zakat. Selanjutnya akan dilaksanaka pemdapingan kepada UPZ Masjid Babul Firdaus untuk memastikan program ini berkelanjutan. Dengan adanya Sistem Informasi Zakat maka pengelolaan Zakat oleh Unit Pengumpul Zakat (UPZ) Masjid Babul Firdaus dapat lebih efisien, efektif, transparan dan akuntabel sehingga kehadiran UPZ berkontribusi dalam menciptakan sinergitas masyarakat kategori muzakki menyelesaikan permasalahan masyarakat lainnya terutama dalam pengentasan kemiskinan.

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Amaluddin, Amaluddin; Satra, Ramdan; Gaffar, Mufila, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Pot tanaman pintar berbasis Internet of Things merupakan sistem yang dapat mendeteksi dan memonitoring kelembaban tanah melalui smartphone. Kemajuan teknologi ini dapat mempermudah masyarakat dalam merawat tanaman hiasnya. Penelitian ini bertujuan untuk merancang pot tanaman pintar brbasis Internet of Things (IoT) untuk tanaman hias Aglonema. Pot ini dilengkapi dengan fitur monitoring kelembaban tanah, ketinggian air pada tangki penampungan, dan sistem peringatan berbasis Buzzer. Dengan memanfaatkan sensor DHT22, Cavacitive Soil Moisture, dan Ultrasonik HC-SR04, data lingkungan seperti kelembaban tanah, suhu, dan ketinggian air pada tangki dapat dipantau secara real-time melalui aplikasi Blynk IoT. Mikrokontroller NodeMCU, ESP8266 digunakan sebagai pusat pengendalian sistem, memungkinkan penyiraman otomatis ketika kelembaban tanah berada dibawah ambang batas. Hasil pengujian menunjukkan bahwa sistem ini efektif dalam memantau kondisi tanaman serta menghemat waktu dan tenaga pengguna dalam merawat tanaman hias. Pot tanaman pintar ini diharapkan menjadi solusi inovatif bagi pecinta tanaman hias untuk meningkatkan keberhasilan perawatan tanaman hias Aglonema

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Fauzan, Muhammad Rifqi; Anraeni; Siska, Ilmawan; Lutfi Budiman, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Era kepelatihan Shin Tae-Yong menjadi momen penting bagi sepak bola Indonesia. Performa Timnas Indonesia di bawah asuhan Shin Tae-Yong menuai beragam respons di media sosial X. Penelitian ini bertujuan untuk menganalisis sentimen netizen terhadap timnas Indonesia di era Shin Tae-Yong dengan menggunakan algoritma Support Vector Machine (SVM). Dataset yang digunakan dalam penelitian ini terdiri dari 398 tweet yang dikumpulkan dari platform media sosial X, namun setelah prosedur preprocessing, jumlah tersebut berkurang menjadi 391 tweet yang merepresentasikan opini dan pandangan netizen tentang timnas Indonesia di era Shin Tae-Yong. Dari data yang dianalisis, 200 data (51,15%) menunjukkan sentimen positif terhadap timnas Indonesia di era Shin Tae-Yong, sementara 191 data (48,85%) menunjukkan sentimen negatif. Proses pengumpulan data melibatkan pemilihan tweet yang relevan dengan menggunakan keyword terkait timnas Indonesia di era Shin Tae Yong, diikuti dengan tahap preprocessing untuk membersihkan data. Evaluasi dilakukan dengan mengukur akurasi, presisi, recall, dan F1-score setiap algoritma dalam mengklasifikasikan sentimen tweet sebagai positif atau negatif. Penerapan metode SVM dalam analisa sentimen tentang timnas Indonesia di era Shin Tae-Yong berhasil mencapai tingkat accuracy sebesar 63,29%, precision sebesar 65%, recall sebesar 65% dan F1-Score sebesar 63%. Hasil penelitian menunjukkan bahwa algoritma SVM secara signifikan relatif baik dalam mengenali dan mengklasifikasikan sentimen terkait timnas Indonesia di era Shin Tae-Yong. Penelitian ini dapat memberikan wawasan penting mengenai pemilihan algoritma machine learning yang optimal untuk analisis sentimen dan dapat membantu peneliti dalam memilih metode yang tepat untuk analisis sentimen di berbagai topik

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Afriani; Alwi; Erick Irawadi, Program Studi Teknik Informatika, Fakultas Ilmu Komputer, Universitas Muslim Indonesia, Jl. Urip Sumoharjo km.05, Makassar dan 90231, Indonesia

Abstract

KALAM is a web-based online learning platform provided by Universitas Muslim Indonesia (UMI) and hosted on servers managed by the Center for Data and Information Technology (PDTI). In practice, KALAM still exhibits security weaknesses that could be exploited by unauthorized parties for personal gain. One identified weakness lies on the login page, which lacks a CAPTCHA mechanism, thereby enabling brute-force and SQL injection attacks. Potential abuses include altering and exfiltrating data. The researcher employed initial seed data in the form of usernames to identify accounts that could be forcibly accessed. Two outcomes were observed: a response value of 200 indicates that the account can be attacked, whereas a value of 303 signifies that the account is blocked by the firewall

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Indra; Dolly, Alwi; Erick Irawadi,

Abstract

Program Pengabdian Kepada Masyarakat (PKM) ini dilaksanakan dengan tujuan utama untuk meningkatkan literasi digital bagi para pengelola dan anggota Rumah Baca Pangngadakkang. Intervensi ini dinilai penting karena peran rumah baca sebagai pusat literasi semakin krusial di tengah tantangan literasi digital yang ada di Indonesia. Revolusi digital yang kian pesat menuntut kompetensi literasi dan digital untuk menghadapi tumbuhnya masyarakat informasi dan jaringan. Oleh karena itu, PKM ini berfokus pada pelatihan penggunaan aplikasi Canva untuk mendorong kemampuan kreatif dalam membuat konten visual yang menarik dan edukatif, serta Microsoft Excel untuk memperkuat keterampilan dalam pencatatan dan pengelolaan data rumah baca secara digital. Pelaksanaan kegiatan melalui tahapan observasi, sosialisasi, pelatihan, dan evaluasi. Hasil evaluasi terhadap 22 peserta menunjukkan perbaikan nilai rata-rata posttest yang cukup mencolok untuk Canva yaitu 80.00 dari pretest 65.00, dan untuk Microsoft Excel naik drastis menjadi 80.45 dari pretest 45.00. Keberhasilan program ini didukung oleh fasilitas yang memadai dan antusiasme tinggi dari para peserta, serta dampak langsungnya terlihat pada peningkatan kapasitas literasi digital dan manajemen administrasi rumah baca.

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Fattah; Farniwati, Sugiarti; Sugiarti, Irawati; Irawati, Hayati; Lilis Nur

Abstract

Pengabdian masyarakat ini dilaksanakan di TPA Nurul Iman Pannara, Kota Makassar, yang menghadapi keterbatasan dalam pemanfaatan teknologi digital pada pembelajaran huruf hijaiyah. Kegiatan dilakukan melalui pelatihan, pendampingan, dan praktik penggunaan aplikasi Augmented Reality (AR) kepada 21 guru dan asisten guru. Aplikasi AR ini menampilkan 30 huruf hijaiyah dalam bentuk tiga dimensi (3D), dilengkapi audio pelafalan, menu petunjuk, serta kuis interaktif untuk menguji pemahaman santri. Hasil implementasi menunjukkan adanya peningkatan kompetensi guru dalam mengoperasikan teknologi AR serta munculnya variasi metode pembelajaran yang lebih menarik dan interaktif. Bagi santri, penggunaan aplikasi AR mempermudah pemahaman bentuk huruf, meningkatkan motivasi belajar, serta memperkuat daya ingat melalui visualisasi tiga dimensi. Evaluasi dengan kuesioner terhadap 19 responden menghasilkan skor rata-rata 4,25 (kategori tinggi), yang menegaskan respon positif terhadap penggunaan aplikasi ini. Dengan demikian, kegiatan ini berhasil meningkatkan kompetensi guru sekaligus efektivitas pembelajaran huruf hijaiyah, serta mendukung transformasi digital pendidikan nonformal.

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Nurhayati; Lilis, Student Civil Engineering Doctoral Program; Dewi, Sri Murni, Doctor of Civil Engineering, Professor,Universitas Brawijaya MT. Haryonostr., 167, Malang East Java, Indonesia, 65145; Murti, Wisnu; Nuralinah, Devi, Doctor of Civil Engineering, Universitas Brawijaya MT. Haryonostr., 167, Malang East Java, Indonesia, 65145

Abstract

This paper presents the study of the mechanical performance of finger jointed laminated timber beams, unreinforced and reinforced with Carbon Fiber Reinforced Polymer, made from waste Merbau wood as an alternative to conventional structural materials. The present study focuses on the ultimate load capacity, modulus of elasticity, modulus of rupture, and load-deflection behavior of laminated beams with face butt and face finger joint orientations against solid beams. Tests were conducted by the four-point bending method according to ASTM D198-02. The results indicated that solid beams had the highest load bearing capacity compared to finger-jointed laminated beams. CFRP reinforcement increased the load capacity by 7.15% for face butt orientation and 38.58% for face finger orientation. CFRP reinforced face finger joints showed a significant increase in modulus of elasticity (MOE) and modulus of rupture (MOR) compared to face butt joints, indicating the effectiveness of CFRP reinforcement in certain orientations. Load-deflection analysis shows that CFRP-reinforced beams exhibit better ductility than unreinforced beams, with peak deflection increasing by 27.2% for face-butt and 26.0% for face-finger. Results confirm that CFRP reinforcement can enhance finger-jointed laminated beams; however, despite these improvements, the reinforced laminated beams still do not reach the strength level of solid beams, with the maximum load capacity and bending moment being approximately 31% of the solid beam values. This study offers insights into the development of robust, efficient, and sustainable wood-based building materials. Furthermore, the findings indicate that finger-jointed laminated wood, produced from waste cuttings from the wood processing industry, possesses the potential to be developed into structural building materials, thereby enhancing the value of wood waste

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Mubaraka, Muhammad Afdhal; Fattah, Farniwati; Azis, Huzain, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Perkembangan teknologi Internet of Things (IoT) telah membawa dampak signifikan dalam kehidupan sehari-hari, terutama dalam pengelolaan rumah. Tugas akhir ini memiliki tujuan untuk merancang dan membangun prototipe smart home berbasis ESP32 yang dilengkapi dengan fitur sistem keamanan pintu, lampu pintar, dan AC pintar. ESP32 dipilih sebagai mikrokontroler karena kemampuannya dalam koneksi Wi-Fi dan Bluetooth, serta performanya yang baik dalam aplikasi IoT. Penelitian ini menggunakan metode perancangan prototipe yang mencakup analisis kebutuhan dan perancangan sistem. Hasil dari perancangan ini menunjukkan bahwa prototipe yang dibangun dapat berfungsi dengan baik dalam mengontrol akses pintu, pencahayaan, dan suhu ruangan secara efisien. Perancangan ini juga menunjukkan bahwa sistem dapat dioperasikan dengan tingkat respons yang cepat, yaitu kurang dari 2 detik untuk setiap perintah yang diberikan. Dengan demikian, penelitian ini tidak hanya memberikan solusi praktis untuk meningkatkan kenyamanan dan keamanan rumah, tetapi juga berkontribusi terhadap pengembangan teknologi smart home di Indonesia

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Irsana, Muhammad Alif; Fattah, Farniwati; Syafie, Lukman, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Sejak 2019, perkuliahan di Fakultas Ilmu Komputer Universitas Muslim Indonesia (FIKOM UMI) didukung oleh sebuah aplikasi Learning Management System berbasis website yang diberi nama Kalam (kalam.umi.ac.id). Seiring berjalannya waktu, respon pengguna terbentuk sehingga mendorong perlunya analisis pengalaman pengguna sebagai dasar pertimbangan dalam perbaikan dan perkembangan lebih lanjut. Penelitian ini bertujuan untuk mengetahui pengalaman pengguna selama menggunakan LMS Kalam. Dimensi yang digunakan sesuai dengan karakteristik LMS Kalam sebagai platform Pendidikan, sehingga ada 5 dimensi yang digunakan yaitu general ux, ease of use, interactivity, engagement, assignment & assessment. Partisipan pada penelitian ini berjumlah 10 orang pengguna LMS Kalam. 50 kurva yang digambarkan partisipan dimana pada masing – masing partisipan menggambarkan 10 kurva. Berdasarkan hasil penelitian ini faktor yang berpengaruh dalam perubahan pengalaman pengguna pada LMS Kalam antara lain adalah tampilan website yang simpel, website mudah digunakan, dan akses Kalam yang sulit Ketika banyak pengguna yang aktif diwaktu yang sama

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Abdullah, Syahrul Mubarak; Sumardin, Andi, Fakultas Ilmu Komputer UMI, Jalan Urip Sumiharjo KM 5, 90231, Indonesia

Abstract

Program Pengabdian kepada Masyarakat (PkM) ini dirancang untuk memperkuat kompetensi kewirausahaan santri melalui penerapan konsep Santripreneur yang terintegrasi dengan teknologi tepat guna pada budidaya ayam petelur. Pesantren sebagai lembaga pendidikan Islam memiliki potensi besar dalam mencetak sumber daya manusia yang religius, mandiri, dan berdaya saing. Namun, sejumlah tantangan masih dihadapi, seperti minimnya keterampilan bisnis praktis, kurangnya akses terhadap teknologi budidaya modern, serta terbatasnya pemahaman mengenai manajemen usaha berbasis syariah. Kegiatan ini meliputi pelatihan kewirausahaan, penerapan prinsip muamalah dalam bisnis, pendampingan teknik budidaya ayam petelur, serta penggunaan teknologi seperti Pengenalan beberapa marketplace media sosial dan pengambilan gambar dan video. Program ini diikuti oleh 25 santri yang terlibat dalam sesi teori dan praktik langsung di masjid dalam wilayah pesantren. Hasil evaluasi menunjukkan peningkatan kemampuan yang signifikan, di mana 88% peserta memahami konsep muamalah Syariah Islam, 84% menguasai teknik pemeliharaan ayam yang benar, dan 79% mampu mengoperasikan perangkat teknologi sederhana untuk mendukung pemasaran di masa yang akan datang. Secara keseluruhan, program ini berhasil membangun pola pemberdayaan pesantren yang relevan dengan kebutuhan ekonomi modern. Integrasi nilai agama, keterampilan teknis, dan teknologi memungkinkan pesantren untuk mengembangkan unit usaha berkelanjutan sekaligus menjadi model pembelajaran kewirausahaan yang dapat direplikasi oleh pesantren lain di Indonesia.

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As’ad, Ihwana, Department of Information Systems, Faculty of Computer Science, Universitas Muslim Indonesia, Jl. Urip Sumoharjo KM5, Makassar and 90231, Indonesia; Rahmatullah, Andi Muhammad; Abdullah, Syahrul Mubarak; Fahmi, Department of Informatics Engineering, Faculty of Computer Science, Universitas Muslim Indonesia, Jl. Urip Sumoharjo KM5, Makassar and 90231, Indonesia; Pakka, Hariani Ma'tang, Department of Electrical Engineering, Faculty of Engineering, Universitas Muslim Indonesia, Jl. Urip Sumoharjo KM5, Makassar and 90231, Indonesia; Andiyan, Department of Architecture, Faculty of Science and Engineering, Universitas Faletehan Indonesia, Kompleks Surapati Core Blok-K1, Bandung

Abstract

Traditional clove essential oil distillation remains inefficient due to manual labor dependency, imprecise oil-water separation, and inconsistent product quality. Addressing these limitations, this study aims to design and develop a smart, Internet of Things (IoT)-based system named AquaClove to automate and optimize the distillation process. The system integrates an ESP32 microcontroller, ultrasonic sensors, and solenoid valves, enabling precise fluid level detection and automated oil-water separation. Using the Arduino IoT Cloud, the system supports real-time monitoring and remote control, enhancing operational transparency and scalability. results indicate that the system achieved a 32% reduction in total distillation time (from 4.2 to 2.9 hours), 66.7% reduction in labor requirements (from 3 to 1 personnel), and 66.7% reduction in oil loss per 10-liter batch (from 0.6 L to 0.2 L). The ultrasonic sensors consistently detected liquid levels with an average measurement deviation of less than ±2 mm, while solenoid valves responded within 0.8 seconds of command input. These outcomes demonstrate significant improvements in process efficiency, separation precision, and system responsiveness. Furthermore, the modular container design promotes gravity-assisted separation, enhancing energy efficiency and reducing mechanical complexity. The remote monitoring feature allows users to access real-time data on fluid levels and system status, ensuring reliable operation with minimal manual supervision. AquaClove thus demonstrates how integrating ultrasonic sensing and IoT technologies can modernize traditional agricultural processes. This study contributes a scalable and sustainable solution to the essential oil industry, particularly in small- and medium-scale clove oil production.

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Abdullah, Syahrul Mubarak; Sumardin, Andi; Munira; Anraeni, Siska, Universitas Muslim Indonesia, Jl. Urip Sumiharjo KM.5, Makassar, 90231

Abstract

Industri halal berkembang pesat sebagai pilar pertumbuhan ekonomi global, termasuk di Indonesia, didorong oleh populasi Muslim yang besar, kesadaran akan konsumsi halal, dan dukungan program pemerintah. Sertifikasi halal menjadi aspek kritis, terutama bagi UMKM, namun masih terdapat kendala seperti keterbatasan akses dan pemahaman. Untuk mengatasi hal ini, Badan Penyelenggara Jaminan Produk Halal (BPJPH) meluncurkan program Sertifikat Halal Gratis (SEHATI) melalui aplikasi SIHALAL. Namun, implementasinya menghadapi tantangan, seperti minimnya pengetahuan pelaku usaha tentang prosedur sertifikasi. Studi ini dilakukan di Desa Pucak, Kabupaten Maros, dengan melibatkan pelaku UMKM dan aparat desa. Metode yang digunakan bersifat partisipatif dan edukatif, meliputi presentasi, simulasi penggunaan aplikasi SIHALAL, serta diskusi kelompok. Hasil evaluasi menunjukkan bahwa mayoritas peserta (48 orang) percaya SIHALAL akan membantu UMKM, dan 42 peserta menyatakan akan menggunakan layanan SIHALAL BPJPH. Namun, sebagian kecil responden masih ragu, menunjukkan perlunya sosialisasi lebih intensif. Hasil pelatihan ini berhasil meningkatkan pemahaman peserta tentang pentingnya sertifikasi halal dan penggunaan aplikasi SIHALAL. Meskipun demikian, upaya lebih lanjut dalam edukasi dan promosi diperlukan untuk meningkatkan penerimaan dan pemanfaatan layanan ini, sehingga dapat mendukung pertumbuhan industri halal di Indonesia.

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Wanda; Abdullah, Syahrul Mubarak, Universitas Muslim Indonesia

Abstract

A house is a place of residence for humans, and along with the advancement of time, houses have evolved into various types, including two-story and three-story buildings. Electricity expenses can increase due to the large number of electrical connections in multi-story houses. For example, a two-story house typically contains 3 to 4 bedrooms, which requires occupants to go up and down the stairs to turn off the lights. Often, lights are left on when residents forget to switch them off before leaving the house. A solution to this issue is to develop an automatic light control system using a sound sensor. This system allows the lights to be turned on automatically, even when the occupant is on the first floor or when the house is unoccupied. This research aims to design an automatic lamp control device using a sound sensor based on Arduino. The system produced by this research enables users to activate lights automatically by mentioning the name of the room, eliminating the need to move between floors to operate lights on the second or third floor. Based on the results of room and distance testing, an accuracy of 80% was achieved for room-specific tests, 70% for distance testing, and 68.82% for sound sensor detection accuracy. These results indicate that the design and implementation of the system have performed well.

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Purnawansyah; Manga, Abdul Rachman, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Sampah menjadi permasalahan besar di Indonesia seiring dengan meningkatnya populasi dan konsumsi masyarakat. Untuk mengatasi permasalahan ini, klasifikasi sampah yang akurat diperlukan guna mendukung pengelolaan dan daur ulang yang lebih efektif. Penelitian ini menerapkan algoritma K-Nearest Neighbors (KNN) untuk mengklasifikasikan sampah berdasarkan jenisnya, dengan menggunakan tiga metrik jarak: Euclidean, Manhattan, dan Minkowski. Dataset yang digunakan terdiri dari 997 gambar yang terbagi menjadi dua kelas, yaitu kertas dan kardus. Data dibagi menjadi 80% untuk pelatihan dan 20% untuk pengujian. Proses preprocessing meliputi resize gambar ke ukuran 128x128 piksel serta normalisasi data. Model dievaluasi menggunakan metrik akurasi, presisi, recall, dan F1-score. Hasil penelitian menunjukkan bahwa jarak Manhattan memberikan performa terbaik dengan akurasi 83%, diikuti oleh Euclidean dengan 75,50%, dan Minkowski dengan 66,50%. Temuan ini menegaskan bahwa pemilihan metrik jarak dalam algoritma KNN sangat memengaruhi kinerja model. Dengan pendekatan ini, diharapkan dapat meningkatkan efisiensi sistem pengelolaan sampah berbasis teknologi, sehingga mendukung upaya daur ulang yang lebih baik dan ramah lingkungan

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Ariani, Lisna; Indra, Dolly; Umar, Fitriyani, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Cengkeh merupakan salah satu komoditas unggulan di sektor pertanian Indonesia yang memiliki nilai ekonomi tinggi, namun rentan mengalami penurunan kualitas akibat kontaminasi jamur selama proses panen dan pascapanen. Selama ini, proses klasifikasi cengkeh berjamur dan tidak berjamur masih dilakukan secara manual, yang cenderung tidak efisien, subjektif, dan berpotensi menimbulkan inkonsistensi, terutama ketika harus menangani volume data dalam jumlah besar. Oleh karena itu, penelitian ini dirancang untuk mengembangkan sistem klasifikasi otomatis berbasis kecerdasan buatan menggunakan metode Convolutional Neural Network (CNN) dengan arsitektur InceptionV3. Penelitian akan dilakukan di Kecamatan Lasusua, Kabupaten Kolaka Utara, dengan menggunakan dataset sebanyak 1.000 citra digital cengkeh yang diambil dari dua pose berbeda. Seluruh citra akan melalui tahapan preprocessing dan augmentasi guna memperkuat kualitas dan keragaman data, sebelum digunakan dalam proses pelatihan model CNN. Evaluasi terhadap performa model akan dilakukan dengan mengukur nilai akurasi, presisi, recall, dan F1-score untuk menentukan sejauh mana efektivitas sistem dalam mengklasifikasikan cengkeh ke dalam dua kelas, yaitu berjamur dan tidak berjamur. Diharapkan hasil dari penelitian ini dapat memberikan kontribusi nyata terhadap peningkatan efisiensi, akurasi, dan objektivitas dalam proses sortasi cengkeh serta menjadi langkah awal dalam penerapan teknologi deep learning untuk mendukung digitalisasi mutu hasil pertanian, khususnya pada komoditas strategis seperti cengkeh

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Purnawansyah; Julisa; Darwis, Herdianti; Astuti, Wistiani; Umar, Fitriyani; Sugiarti, Faculty of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Mangrove ecosystems play an important role in maintaining the balance of the coastal environment, both from ecological and economic aspects. To support the conservation of this ecosystem, accurate information on the classification of mangrove species is needed. The manual identification process that is still widely used has limitations in efficiency and scale. Therefore, this research proposes a deep learning-based mangrove leaf image classification approach using VGG16 architecture as a feature extractor, which is then classified using two machine learning algorithms, namely Support Vector Machine (SVM) and Random Forest. The dataset used consists of leaf images of three mangrove species, namely Avicennia alba, Rhizophora apiculata, and Sonneratia alba, which were collected directly. To improve the generalization performance of the model, various image augmentation scenarios were performed, including zoom, flip, rotation, and a combination of the three. Performance evaluation was conducted using accuracy, precision, recall, and F1-score metrics using a k-fold cross-validation scheme. The results show that the combination of VGG16 + SVM with zoom augmentation provides the highest accuracy of

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Lahuddin, Harlinda; Muliawan, Muh. Raihan Alif; Darwis; Herdianti; Jabir, Sitti Rahmah; Adawiyah, Rabiatul, Faculty of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia; Takemoto, Kazuhiro, Department of Bioscience and Bioinformatics, Kyushu Institute of Technology Fukuoka, Japan

Abstract

Cryptocurrency markets exhibit significant volatility and nonlinearity, creating difficulties for precise price prediction. This study assesses and contrasts six deep learning models LSTM, CNN, Transformer, TCN, CNN-LSTM, and TCN-LSTM for forecasting the closing prices of four popular cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), and Litecoin (LTC). Each model utilizes historical OHLCV data of 5 years, processed through a uniform preprocessing pipeline that encompasses normalization, sliding window segmentation, and an 80: 20 train-test division. Experimental findings indicate that the hybrid TCN-LSTM model exceeds the performance of all other models based on evaluation metrics including MAE, RMSE, MAPE, and \boldsymbol{R}^{2}, showing its capacity to grasp both short- and long-term time patterns. This study additionally highlights the effectiveness of parallel hybrid architectures, particularly the TCN-LSTM model. These findings contribute to the expanding body of research on deep learning applications in financial forecasting and offer practical guidance for developing robust cryptocurrency prediction models.

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Purnawansyah; Darwis, Herdianti, Universitas Muslim Indonesia, Makassar, Indonesia; Raja, Roesman Ridwan, Kyushu Institute of Technology, Iizuka City, Jepang

Abstract

This research explores public sentiment towards the Indonesian police using sentiment analysis and machine learning techniques. The study addresses the challenge of understanding public opinion based on social media comments related to significant police cases. The aim is to compare reported satisfaction levels with actual public sentiment. Utilizing the Indonesian RoBERTa base IndoLEM sentiment classifier, comments were analyzed and preprocessed. The classification was conducted using Random Forest (RF) and Complement Naive Bayes (CNB) models, incorporating unigram and bi-gram features. Oversampling techniques were applied to handle data imbalance. The best-performing model, Random Forest with bi-gram features, achieved high evaluation scores, including a precision of 0.91 and accuracy of 0.91. The findings reveal significant insights into public opinion, contributing to improved law enforcement strategies and public trust.

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Kurnia, Arif; Harlinda; Darwis, Herdianti, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Gojek adalah aplikasi layanan on-demand yang telah menjadi salah satu platform terbesar di Asia Tenggara dengan jutaan pengguna aktif dan 5 juta ulasan di App Store. Ulasan ini menjadi sumber informasi penting untuk mengevaluasi dan meningkatkan layanan. Penelitian ini bertujuan untuk menganalisis sentimen ulasan pengguna Gojek dengan mengelompokkan ulasan menjadi lima kelas sentimen: "Sangat Puas", "Puas", "Cukup", "Buruk", dan "Sangat Buruk". Metode yang digunakan meliputi K-Nearest Neighbors (KNN), Naive Bayes, dan Support Vector Machine (SVM). Setelah melakukan text preprocessing, ketiga metode tersebut dievaluasi berdasarkan accuracy, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa model SVM dengan kernel Linear mencapai akurasi tertinggi sebesar 79.00%, diikuti kernel RBF dengan precision tertinggi sebesar 83.85%. Model Naive Bayes menunjukkan performa cukup baik dengan akurasi 78.00%, sementara KNN memiliki akurasi terendah sebesar 69.25%. Berdasarkan hasil ini, SVM, khususnya dengan kernel Linear dan RBF, terbukti menjadi metode paling efektif dalam menganalisis sentimen pengguna Gojek, memberikan wawasan yang lebih akurat untuk perbaikan layanan

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Purnawansyah, Department of Electrical Engineering and Informatics, Universitas Negeri Malang, Jl. Semarang, Malang, 65145, Indonesia; Department of Computer Science, Universitas Muslim Indonesia, Jl. Urip Sumoharjo, Makassar, 90231, Indonesia; Darwis, Herdianti; Azis, Huzain, Department of Computer Science, Universitas Muslim Indonesia, Jl. Urip Sumoharjo, Makassar, 90231, Indonesia; Wibawa, Aji Prasetya; Widiyaningtyas, Triyanna, Department of Electrical Engineering and Informatics, Universitas Negeri Malang, Jl. Semarang, Malang, 65145, Indonesia; Haviluddin, Department of Computer Science, Universitas Mulawarman Jl. Kuaro Gunung Kelua, Samarinda, 75119, Indonesia

Abstract

This study aims to assess the effectiveness of various algorithms in the realms of supervised and semi-supervised learning applied to three multiclass facial image datasets: JAFFE, Georgia tech, and Yale. The datasets were partitioned into proportions of 80:20, 75:25, and 50:50 for supervised learning, while semi-supervised learning was conducted with labelled and unlabeled data ratios of 20:80, 25:75, and 50:50. The evaluated algorithms include convolutional neural networks (CNNs), decision tree, long short-term memory, K-nearest neighbors (K-NNs), multilayer perceptron, and support vector classifier (SVC), each with varying parameters. Experimental outcomes reveal that the performance of models depends on the dataset partitioning strategies and the type of algorithms used. Specifically, linear and polynomial SVC consistently yield favorable results in supervised learning, particularly demonstrating efficacy on the Georgia tech dataset. Conversely, on the JAFFE and Yale dataset, linear SVC and K-NN emerge as optimal choices. The inclusion of semi-supervised learning enhances insights, particularly evident in the Georgia tech dataset, where the combination of labeled and unlabeled data significantly improves accuracy, especially when leveraging linear SVC algorithm. Although there are some instances of sub-optimal performance in certain algorithms like CNN on specific datasets, this research provides comprehensive insights into the effectiveness of various models in contexts of limited-label learning. The implications of these findings are crucial in advancing the development of adaptive and robust facial recognition systems, especially in navigating datasets characterized by diverse variations and complexities.

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Harlinda, L; Rahma, Dewi Ernita; Azis, Huzain; Irawati; Ramdaniah, Faculty of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia; Isnan, Mahmud, Computer Science Department School of Computer Science Bina Nusantara University, Jakarta, Indonesia

Abstract

The objective of this work is to develop an automatic classification system that can accurately and consistently distinguish between healthy and Curvularia-infected oil palm leaves, even in the face of inadequate data. The proposed approach uses the ResNet50 model as a feature extractor in conjunction with more conventional machine learning techniques like Random Forest and K-Nearest Neighbors. With a focus on implementation feasibility and efficiency in real-world applications, this combination has shown competitive performance when compared to the end-to-end ResNet50 approach. With these contributions, this study offers methods for diagnosing plant diseases that are intelligent, flexible, and resource-efficient, facilitating digital transformation in the agriculture industry. These findings highlight the potential of hybrid deep learning systems in providing accurate, fast, and cost-effective disease diagnosis solutions for oil palm plantations, particularly in tropical environments with limited resources.

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Suzanti, Ika Oktavia; Kamil, Fajrul Ihsan; Rochman, Eka Mala Sari; Rachman, Fika Hastarita; Solihin, Firdaus, Informatics Engineering Department, University of Trunojoyo Madura, Bangkalan, Indonesia; Huzain Azis, Malaysian Institute of Information Technology (MIIT), Universiti Kuala Lumpur, Kuala Lumpur, Malaysia; Suni, Alfa Faridh, Computer Science Department, Newcastle University, United Kingdom

Abstract

Hidden paradise is a term that aptly describes the island of Madura, which offers diverse tourism potential. Through the Google Maps application, tourists can access sentiment-based information about various attractions in Madura, serving both as a reference before visiting and as evaluation material for the local government. The Multinomial Naïve Bayes method is used for text classification due to its simplicity and effectiveness in handling text mining tasks. The sentiment classification is divided into three categories: positive, negative, and mixed. Initial analysis revealed an imbalance in sentiment data, with most reviews being positive. To address this, sampling techniques—both oversampling and undersampling—were applied to achieve a more balanced data distribution. Additionally, the Adaptive Boosting ensemble method was used to enhance the accuracy of the Multinomial Naïve Bayes model. The dataset was split into training and testing sets using ratios of 60:40, 70:30, and 80:20 to evaluate the model’s stability and reliability. The results showed that the highest F1-score, 84.1%, was achieved using the Multinomial Naïve Bayes method with Adaptive Boosting, which outperformed the model without boosting, which had an accuracy of 76%.

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Syafie, Lukman; Herman; As' ad, Ihwana; Universitas Muslim Indonesia

Abstract

This community service program aims to improve digital literacy among elementary school students in Kedah, Malaysia through introducing robotics and Scratch programming. The main issue faced was students' limited exposure to computational thinking and STEM-based learning. Through intensive training provided by Universitas Muslim Indonesia, students from 13 schools were equipped with fundamental skills in visual programming using Scratch and basic robotics concepts. The training was officially opened by the Special Officer to the Minister of Education Malaysia and covered introduction to computational thinking, hands-on Scratch programming, and educational robotics demonstrations. Results showed significant improvement in students' understanding of programming concepts and increased enthusiasm towards technology. Additionally, an educational module was created for continued learning. The program successfully enhanced students' digital literacy and received strong appreciation from school principals who expressed interest in ongoing collaboration for similar activities.

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Junaid, Annisa; Ilmawan, Lutfi Budi; Azis, Andi Adillah Firstani, Universitas Muslim Indonesia

Abstract

Pemanfaatan kayu sebagai bahan konstruksi masih sangat tinggi di Indonesia, namun proses pemilahan kayu di tingkat industri kecil hingga menengah masih dilakukan secara visual dan subjektif. Hal ini berdampak pada ketidaksesuaian antara mutu kayu dan penggunaannya di lapangan. Kegiatan pengabdian ini bertujuan untuk memberikan pelatihan teknik pemilahan kayu berbasis sains menggunakan metode stress wave velocity kepada pelaku usaha kayu. Metode ini bersifat non-destruktif, mudah diterapkan, dan dapat memperkirakan kekuatan mekanis kayu berdasarkan kecepatan rambat gelombang tegangan yang terukur melalui aplikasi Wave Spectra dan Wood Quality Detector. Pelatihan dilaksanakan di UD. Cahaya Putri, Katimbang, Paccerakkang, Makassar. Hasil kegiatan menunjukkan peningkatan pengetahuan mitra sebesar 56% mengenai konsep kekuatan kayu dan teknik pemeriksaannya, serta meningkatnya keterampilan penggunaan alat uji sederhana berbasis smartphone. Kegiatan ini diharapkan menjadi langkah awal dalam upaya standarisasi pemilahan kayu secara ilmiah di tingkat UMKM untuk mendukung industri konstruksi berkelanjutan.

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Syaifullah, Muhammad Andi; Hayati, Lilis Nur; Ilmawan, Lutfi Budi, Informatics Engineering,Faculty of Computer Science, University of Muslim Indonesia, Makassar City, Indonesia

Abstract

Royal Cafe Makassar still relies on a manual system for managing queues and processing orders, which leads to long waiting lines, delayed service, and frequent recording errors. This study aims to design and implement a mobile-based queue management application using the First Come First Served (FCFS) method to create a more efficient, structured, and transparent ordering process. System development follows the Waterfall model, consisting of requirement analysis, system design, implementation, testing, and maintenance phases. The application provides features for registration and login, digital menu browsing, ordering, payment processing, queue monitoring, and a role-based workflow involving administrators, baristas, and waiters. Functional validation is conducted using black-box testing, while usability evaluation employs the System Usability Scale (SUS). The results show that every major function of the application operates according to the specified requirements. The SUS evaluation, involving 73 respondents, yields an average score of 82.35, which falls into the “very good” and “acceptable” categories, indicating high usability and user satisfaction. The FCFS mechanism effectively addresses operational issues by ensuring fair and consistent service order based on customer arrival time, reducing manual workload, and minimizing ordering errors. Overall, the developed system provides a practical digital solution for improving café service efficiency and can serve as a reference model for similar small-scale businesses adopting queue management applications.

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Mansyur, St. Hajrah; Hidayah, Nurul, Sistem Informasi, Universitas Muslim Indonesia, Makassar, Indonesia; Ilmawan, Lutfi Budi; Putra, Meydil Eka Syah; Rahmansyah, Moh. Fathur, Teknik Informatika,Universitas Muslim Indonesia,Makassar,Indonesia; Nengsi, Ratika, Pendidikan Agama Islam, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Pendidikan inklusif bagi penyandang disabilitas masih menghadapi keterbatasan media pembelajaran adaptif, khususnya pada pembelajaran huruf dan harakat hijaiyah yang menuntut kompleksitas komunikasi visual, auditori, dan taktil. Kegiatan Pengabdian kepada Masyarakat (PkM) ini bertujuan mengembangkan dan mengimplementasikan media digital multisensori HarakaTouch sebagai solusi inovatif di SLB Negeri 1 Makassar. Metode pelaksanaan dirancang melalui tujuh tahapan utama: HarakaConnect, HarakaTrain, HarakaTech, HarakaGuide, HarakaTrack, HarakaReflect, dan HarakaSustain, dengan melibatkan 16 siswa difabel serta 15 guru. Hasil menunjukkan adanya peningkatan signifikan pada capaian siswa: skor pemahaman huruf dan harakat meningkat dari 42% menjadi 81% (+39%), kemandirian belajar dari 2,1 menjadi 3,6, serta konsentrasi dari 50% menjadi 82%. Pada guru, pemahaman strategi pembelajaran inklusif naik dari 55% menjadi 88% (+33%), dan keterampilan penggunaan media dari 2,3 menjadi 3,7. Keberhasilan program ditandai dengan terbentuknya komunitas Guru Hijaiyah Inklusi, serta legitimasi hukum melalui sertifikat HAKI. Temuan ini membuktikan bahwa HarakaTouch efektif meningkatkan literasi hijaiyah difabel, memperkuat kapasitas guru, dan berpotensi dikembangkan sebagai educational technology inklusif berskala nasional.

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Kurniati, Nia, Universitas Muslim Indonesia, Jl.Urip Sumoharjo Km.05, Makassar dan 90231, Indonesia; Irawati; Hasnawi, Mardiyyah; Fattah, Farniwati; As'ad, Ihwana; Hasanuddin, Tasrif, Fakultas Ilmu Komputer, Universitas Muslim Indonesia, Jl.Urip Sumoharjo Km.05, Makassar dan 90231, Indonesia; Mazhud, Nurfathana, Fakultas Sastra, Universitas Muslim Indonesia, Jl.Urip Sumoharjo Km.05, Makassar dan 90231, Indonesia

Abstract

Kegiatan Pengabdian kepada Masyarakat (PKM) ini dilaksanakan di Taman Pendidikan Al-Qur’an (TPA) Darul Ilmi, Kelurahan Benteng Selatan, Kepulauan Selayar. Tujuan kegiatan ini adalah untuk memberikan edukasi kepada siswa-siswi TPA mengenai pengelolaan sampah non-organik menjadi barang kerajinan tangan melalui penerapan aplikasi digital berbasis multimedia interaktif. Permasalahan utama mitra meliputi metode pembelajaran yang masih konvensional dan monoton, serta rendahnya pengetahuan masyarakat dan siswa tentang jenis-jenis sampah dan cara pemanfaatannya. Solusi yang ditawarkan berupa penggunaan aplikasi edukasi yang berisi informasi tentang pengertian, jenis, dampak, dan cara pengolahan sampah non-organik, disertai tutorial pembuatan kerajinan tangan serta permainan edukatif. Metode pelaksanaan kegiatan meliputi observasi, wawancara, studi pustaka, sosialisasi, pelatihan, pendampingan, dan evaluasi melalui penyebaran kuesioner pre-test dan post-test. Hasil kegiatan menunjukkan peningkatan pengetahuan siswa TPA sebesar 67% setelah penerapan aplikasi. Para peserta menjadi lebih aktif, kreatif, dan memahami pentingnya pengelolaan sampah bagi lingkungan. Selain itu, siswa mampu mengolah limbah non-organik menjadi produk kerajinan seperti tempat pensil, vas bunga, dan hiasan dinding. Kegiatan ini tidak hanya meningkatkan keterampilan dan kesadaran lingkungan peserta, tetapi juga memperkenalkan pendekatan pembelajaran berbasis teknologi yang menarik dan efektif di lingkungan pendidikan non-formal. Dengan demikian, penerapan aplikasi ini terbukti bermanfaat dalam menumbuhkan kreativitas serta kepedulian terhadap lingkungan sejak usia dini.

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Lasinrang, Akhmad Petta Lolo; Harlinda; Sugiarti, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Baju Bodo yang berasal dari suku Bugis ini merupakan salah satu baju tertua yang ada di dunia. Baju bodo sebagai warisan budaya Sulawesi Selatan memiliki nilai estetika dan sejarah yang tinggi. Baju Bodo dapat ditemukan di toko-toko yang menyediakan layanan peminjaman. Saat ini peminjaman Baju Bodo masih dilakukan secara manual. Penelitian ini bertujuan untuk merancang sebuah sistem peminjaman baju bodo berbasis Android yang efisien dan mudah digunakan. Namun, saat ini ketersedian aplikasi untuk meminjam baju bodo masih terbatas. Oleh karena itu, penelitian ini diharapkan menjadi solusi, dimana dengan adanya rancangan aplikasi nantinya dapat dikembangkan menjadi aplikasi yang dapat diakses masyarakat yang dapat mempermudah proses peminjaman Baju Bodo. Metode pengembangan sistem yang digunakan adalah metode waterfall, yang terdiri dari tahap analisis, perancangan, implementasi, pengujian, dan pemeliharaan. Tahap analisis dilakukan untuk mengidentifikasi kebutuhan pengguna dan fungsionalitas sistem. Tahap perancangan meliputi perancangan antarmuka pengguna (user interface) dan basis data

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Ashad, Bayu Adrian, Program Studi Teknik Elektro, Fakultas Teknik, Universitas Muslim Indonesia; Ramdaniah, Program Studi Teknik Informatika, Fakultas Ilmu Komputer, Universitas Muslim Indonesia

Abstract

Kegiatan pengabdian kepada masyarakat ini dilaksanakan di UD Tani Nila Nusantara, Kabupaten Pangkep, dengan tujuan meningkatkan efisiensi penggunaan energi listrik pada sistem budidaya ikan nila melalui penerapan teknologi smart meter berbasis Internet of Things (IoT). Permasalahan utama mitra adalah tingginya konsumsi energi akibat pengoperasian aerator, dan pompa air yang berlangsung hampir tanpa henti setiap hari. Untuk mengatasi hal tersebut, diterapkan sistem monitoring energi berbasis IoT yang memungkinkan pengelolaan daya secara real-time. Metode pelaksanaan kegiatan menggunakan pendekatan pendidikan masyarakat dan difusi ipteks melalui pelatihan, instalasi sistem smart meter, serta pendampingan langsung kepada mitra dalam pengoperasian dan analisis data energi. Hasil evaluasi menunjukkan peningkatan pemahaman mitra terhadap efisiensi energi dan kemampuan menggunakan perangkat digital secara mandiri. Selain itu, terjadi penurunan konsumsi listrik rata-rata sebesar 15–20% setelah penerapan sistem. Kegiatan ini tidak hanya meningkatkan efisiensi operasional tambak, tetapi juga memperkuat literasi digital dan kesadaran penggunaan energi berkelanjutan di sektor perikanan. Dengan demikian, penerapan smart meter berbasis IoT dapat menjadi solusi praktis dan aplikatif menuju pengelolaan energi yang cerdas, efisien, dan ramah lingkungan pada usaha tambak ikan air tawar.

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Ramdaniah, Program Studi Teknik Informatika, Fakultas Ilmu Komputer, Universitas Muslim Indonesia; Ashad, Bayu Adrian, Program Studi Teknik Elektro, Fakultas Teknik, Universitas Muslim Indonesia

Abstract

Kegiatan Pengabdian kepada Masyarakat (PkM) ini bertujuan meningkatkan efisiensi dan literasi digital masyarakat petambak melalui pelatihan penggunaan aplikasi Feeder Planner di Kabupaten Pangkep, Sulawesi Selatan. Permasalahan utama yang dihadapi mitra adalah manajemen tambak yang masih dilakukan secara manual tanpa pencatatan berbasis data, sehingga mengakibatkan pemborosan pakan dan sulitnya evaluasi produksi. Metode pelaksanaan menggunakan pendekatan Participatory Action Research (PAR) melalui skema pelatihan dan difusi ipteks dengan pendampingan langsung di lapangan. Kegiatan berlangsung selama empat bulan, dari Agustus hingga November 2025, bekerja sama dengan mitra UD Tani Nila Nusantara dan melibatkan enam orang petambak sebagai peserta. Tahapan kegiatan meliputi observasi kebutuhan, sosialisasi, pelatihan penggunaan aplikasi, pendampingan, serta evaluasi partisipatif. Hasil kegiatan menunjukkan peningkatan kemampuan peserta dalam mengoperasikan aplikasi secara mandiri untuk mencatat jadwal dan riwayat pemberian pakan. Efisiensi pakan meningkat sekitar 15%, dengan konsistensi pencatatan mencapai 83% dan ketepatan jadwal pemberian pakan sebesar 85%. Kegiatan ini membuktikan bahwa pelatihan berbasis aplikasi digital mampu mendorong transformasi manajemen tambak menuju sistem yang lebih efisien, terukur, dan berkelanjutan di kalangan masyarakat petambak.

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Septiawan, M.Fiqry; Anraeni, Siska; Ramdaniah, Informatics Engineering, Computer Sience, Universitas Muslim Indonesia, Indonesia

Abstract

Technological advancements have provided convenience for Generation Z in managing finances; however, many are still not accustomed to recording their financial activities regularly. Shopping receipts, which should serve as proof of transactions, are often ignored or poorly managed, despite their important role in tracking expenses. Therefore, this research aims to develop an Android-based financial recording application capable of handling both manual input and automated recording through receipt scanning using Optical Character Recognition (OCR) technology. The findings indicate that ChatGPT-4o significantly outperforms EasyOCR by providing more consistent accuracy and faster, stable processing, making it a more reliable solution for receipt-based financial recording. Developed using the Agile Kanban method, the application was validated through alpha testing and proven to function properly across all features. Beyond practical benefits for users, this research also contributes to the financial technology literature by demonstrating the integration of large language models (LLM) to enhance OCR performance in mobile finance applications.

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Dolly, Indra, Departement of information Systems, Computer Science, Universitas Muslim Indonesia, Indonesia; Ramdaniah; Ode, Nada Kayatri, Department of Informatic Engineering, Computer Science, Universitas Muslim Indonesia, Indonesia

Abstract

Deforestation in Papua has emerged as a significant environmental concern, attracting considerable attention due to its effects on biodiversity and the livelihoods of indigenous communities. This study seeks to examine public sentiment toward the issue by analyzing comments posted on the YouTube platform, employing the Long Short-Term Memory (LSTM) method. A dataset of 3,000 comments was gathered and processed through several stages, including text cleaning, tokenization, normalization, and Term Frequency–Inverse Document Frequency (TF-IDF) weighting. Subsequently, an LSTM model was developed and assessed using accuracy, precision, recall, and F1-score as evaluation metrics. The results reveal that the LSTM model achieved an accuracy of 88.43%, a precision of 90.01%, a recall of 97.49%, and an F1-score of 93.60%. Nevertheless, signs of overfitting were observed, indicated by lower validation performance compared to training results. These findings demonstrate that the LSTM approach is effective for identifying public opinion regarding deforestation and can serve as a valuable reference in decision-making and the formulation of environmental policies.

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Gaffar, Andi Widya Mufila; Widyawati, Dewi; Fahmi; Ulfiani, Sri, Fakultas Ilmu Komputer Universitas Muslim Indonesia, Indonesia

Abstract

Kacang-kacangan merupakan sumber pangan padat energi yang kaya nutrisi bioaktif, berperan penting dalam menjaga kesehatan serta menurunkan risiko penyakit kronis yang dimana kacang-kacangan memiliki jenis dan varietas yang sangat beragam dengan ciri-ciri seperti warna, bantuk dan karateristik yang berbeda beda. Penelitian ini bertujuan untuk mengembangkan sistem deteksi otomatis jenis kacang-kacangan menggunakan metode Convolutional Neural Network (CNN) berbasis arsitektur Faster Objects, More Objects (FOMO) yang diintegrasikan dengan perangkat berbiaya rendah ESP32-Cam melalui platform Edge Impulse. Empat jenis kacang yang digunakan sebagai objek penelitian meliputi kacang hijau, kacang kedelai, kacang merah, dan kacang tanah, dengan total 530 citra hasil akuisisi data. Proses penelitian meliputi tahapan akuisisi data, pre-processing berupa pelabelan, normalisasi, dan resize, pembagian dataset, pelatihan model dengan 60 epoch dan learning rate 0,001, hingga evaluasi kinerja model. Hasil evaluasi menunjukkan bahwa model mencapai F1 Score rata-rata 90,4%, yang termasuk kategori sangat baik, dengan keseimbangan optimal antara precision dan recall. Pada tingkat per kelas, kacang merah berhasil dikenali sempurna dengan akurasi 100%, kacang hijau memperoleh akurasi 94,4%, sedangkan kacang kedelai dan kacang tanah masing-masing mencapai 88% dan 88,9%. Analisis feature explorer menunjukkan adanya tumpang tindih pada distribusi kacang kedelai dan kacang tanah akibat kemiripan tekstur dan morfologi, sehingga berpotensi menimbulkan kesalahan klasifikasi. Secara keseluruhan, hasil penelitian ini membuktikan bahwa CNN dengan menggunakan ESP32-Cam mampu mendeteksi jenis kacang-kacangan dengan akurasi yang sangat baik.

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As' ad, Ihwana; Hayati, Lilis Nur, Jurusan Sistem Informasi, Universitas Muslim Indonesia, Makassar, Indonesia; Harun, Makmur, School of Languange, Civitation and Philosophy, Collage of Arts and Science, Universiti Utara Malaysia, Malaysia; Azuz, Faidah, Jurusan Agribisnis, Universitas Bosowa, Makassar, Indonesia; Alwi, Muhammad, Jurusan Teknik Mesin, STITEK Dharma Yadi, Makassar, Indonesia; Firmiaty, Sri, Jurusan Peternakan, Universitas Bosowa, Makassar, Indonesia; Sugiarti; Tenripada, A Ulfah, Jurusan Teknik Informatika, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Latar Belakang: Kampung Wisata Purbayan merupakan kawasan heritage yang memiliki potensi besar dalam pengembangan pariwisata berbasis budaya dan sejarah. Keterbatasan masyarakat dalam melakukan promosi mengakibatkan banyak potensi wisata seperti situs sejarah Mataram Islam, arsitektur tradisional Jawa, dan kerajinan lokal belum optimal. Kondisi ini menunjukkan perlunya penguatan kapasitas masyarakat dalam mengelola dan mempromosikan desa wisata secara lebih efektif melalui edukasi, pendampingan, dan pemanfaatan teknologi digital. Tujuan: Pengabdian dilaksanakan untuk mengoptimalkan pemanfaatan potensi budaya, sejarah, dan nilai keagamaan di Kampung Wisata Purbayan dalam pengembangan wisata, dan masih rendahnya penggunaan teknologi digital sebagai media promosi dan edukasi. Metode: Metode yang digunakan adalah sosialisasi dengan melibatkan pemangku kepentingan lokal (pengelola kampung, UMKM, tokoh masyarakat) aktif dalam perancangan, pelaksanaan, dan evaluasi intervensi promosi digital dan edukasi wisata halal. Hasil: Hasil pelaksanaan kegiatan Pengabdian kepada Masyarakat (PkM) Internasional di Kampung Wisata Purbayan menunjukkan bahwa masyarakat memiliki potensi yang kuat dalam mengembangkan wisata berbasis nilai agama dan budaya. Kegiatan yang dilaksanakan pada 12–18 Maret 2023 dan melibatkan 67 peserta dari Indonesia, Malaysia, dan Suriname menghasilkan tiga temuan utama: (1) penguatan identitas religius dan budaya lokal sebagai basis pengembangan wisata, (2) peningkatan kapasitas masyarakat dalam memanfaatkan teknologi digital untuk promosi wisata.

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Rifaat, Muhammad Taufik; Faradibah, Amaliah, Program Studi Teknik Informatika, Fakultas Ilmu Komputer, Universitas Muslim Indonesia, Jl.Urip Sumoharjo km.05, Makassar dan 90231, Indonesia; Fauzi, Achmad Nuri, Program Studi Teknik Elektro, Fakultas Teknik, Universitas Negeri Malang, Malang dan 65145, Indonesia

Abstract

This study aims to analyze and identify vulnerabilities to Distributed Denial of Service (DDoS) and phishing attacks on FIKOM UMI’s Wi-Fi with the access point SSID “UMI Connect,” and to provide recommendations to FIKOM UMI. The method employed is Vulnerability Assessment using the Fluxion tool and a TP-Link wireless adapter. The findings reveal two types of vulnerabilities—packet injection and wireless hijacking—each with medium risk and medium confidence. These weaknesses reside at the SSID layer. Attackers can exploit them to disconnect clients from the network and subsequently perform phishing to obtain the access-point password of the targeted SSID. While some security components on the target access point are functioning properly, several areas still require improvement—specifically, unlimited packet rates per second passing through TCP/UDP data transmissions between users and the access point, which should be rate-limited.

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Nugroho, Afifah Khairunnisa; Hayati, Lilis Nur; Jabir, Sitti Rahmah, Universitas Muslim Indonesia, Indonesia

Abstract

Analisis sentimen terhadap Identitas Kependudukan Digital (IKD) menjadi penting untuk memahami persepsi masyarakat terkait upaya digitalisasi layanan publik di Indonesia. IKD diharapkan dapat meningkatkan efisiensi layanan, namun implementasinya menghadapi tantangan, seperti proses aktivasi offline dan kekhawatiran mengenai keamanan data. Penelitian ini bertujuan untuk membandingkan performa dua algoritma, Naïve Bayes dan Random Forest, dalam menganalisis sentimen dan mengklasifikasikan ulasan pengguna IKD. Proses penelitian dimulai dengan pengumpulan data ulasan dari Google Play Store, dilanjutkan dengan tahap pre-processing, pelabelan berbasis lexicon, pembagian data menjadi data latih dan uji, serta pembobotan TF-IDF. Hasil penelitian menunjukkan bahwa algoritma Random Forest memiliki performa yang lebih unggul dibandingkan Naïve Bayes, dengan akurasi tertinggi 93%, sementara Naïve Bayes mencapai 89%. Random Forest juga menunjukkan performa lebih stabil dengan Precision 0,91, Recall 0,84, dan F1-Score 0,87 untuk kelas positif. Di sisi lain, Naïve Bayes mencatat Precision 0,78, Recall 0,80, dan F1-Score 0,79. Untuk kelas negatif, Random Forest lebih efektif dengan Precision 0,94, Recall 0,97, dan F1-Score 0,96, sedangkan Naïve Bayes memiliki Precision 0,93, Recall 0,92, dan F1-Score 0,93. Penelitian ini menyarankan bahwa performa kedua model dapat lebih ditingkatkan dengan eksplorasi metode pelabelan dan penggunaan algoritma ensemble. Selain itu, variasi pada tahap pre-processing, seperti penggunaan n-gram, dapat membantu meningkatkan akurasi analisis sentimen. Kontribusi penelitian ini adalah memberikan evaluasi komparatif terhadap algoritma populer dalam konteks analisis sentimen terhadap IKD, yang belum banyak diteliti sebelumnya. Hasil ini dapat menjadi acuan praktis dalam memilih algoritma klasifikasi untuk analisis opini publik terhadap layanan digital pemerintah.

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Sugianto, M. Taufiq; Herman; Jabir, Sitti Rahmah, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Perkembangan industri game di Indonesia yang semakin pesat telah meningkatkan kebutuhan akan layanan top-up game yang cepat, aman, dan mudah digunakan. Namun, sebagian besar layanan yang tersedia masih dijalankan secara non-formal melalui media sosial, sehingga berpotensi menimbulkan kendala pada kemudahan penggunaan, keamanan transaksi, dan personalisasi layanan. Penelitian ini bertujuan untuk merancang antarmuka pengguna (User Interface) dan pengalaman pengguna (User Experience) aplikasi top-up game berbasis mobile bernama ChillnFun dengan menggunakan metode Design Thinking yang meliputi lima tahapan: Empathize, Define, Ideate, Prototype, dan Test. Data diperoleh melalui kuesioner yang dibagikan kepada pemain game aktif sebagai target pengguna, kemudian dianalisis untuk mengidentifikasi permasalahan utama. Hasilnya, ditemukan dua masalah utama, yaitu proses transaksi yang rumit dan tampilan antarmuka yang kurang menarik. Solusi yang dihasilkan mencakup penyederhanaan alur transaksi, navigasi yang intuitif, desain UI modern, dan penataan elemen visual yang rapi. Prototipe aplikasi dikembangkan menggunakan Figma dan diuji melalui usability testing menggunakan platform Maze serta evaluasi System Usability Scale (SUS). Hasil pengujian menunjukkan skor rata-rata 93 pada Maze dan skor rata-rata 82 pada SUS, yang termasuk kategori Excellent. Temuan ini menunjukkan bahwa prototipe ChillnFun dapat diterima dengan baik oleh pengguna dan mampu meningkatkan efisiensi, kenyamanan, serta kepuasan dalam proses top-up game

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Fathrurahman, Fauzy; Indra, Dolly; Hasanuddin, Tasrif; Darwis, Herdianti, Universitas Muslim Indonesia, Makassar, Indonesia; Kazuaki, Tanaka, Kyushu Institute of Technology, Iizuka, Japan

Abstract

Background: Waste mismanagement remains a critical issue in Indonesian campuses, where ineffective segregation and collection practices contribute to environmental pollution. Smart technologies offer opportunities to improve waste handling efficiency and monitoring in university environments. Methods: This study developed a smart waste bin prototype that integrates Internet of Things (IoT) sensors, machine learning–based image classification (MobileNetV2 with TensorFlow Lite), GPS tracking, and LoRa communication. The system was designed to classify three types of waste—plastic bottles, snack packaging, and cans—while enabling fill-level monitoring, automated sorting, and real-time location reporting. Results: Experimental results showed strong classification accuracy for plastic bottles (100%), but lower performance for snack packaging (53–80%) and cans (40–67%), especially in low-light conditions or with darker materials. The overall real-time testing accuracy reached 45.1%. LoRa communication provided long-range connectivity but was affected by electromagnetic interference, while GPS tracking was reliable in open areas but inconsistent indoors. Conclusions: The prototype demonstrates the feasibility of integrating AI and IoT for scalable campus waste management. Despite environmental and hardware limitations, it offers a modular framework that can be refined with improved lighting, EMI shielding, and enhanced datasets. This research contributes a practical model for smart campus initiatives and supports the adoption of sustainable waste management practices in higher education environments.

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Hayati, Lilis Nur; Indra, Dolly; Djamereng, Asdar; Suat, Wahyu Kadri Rahmat; Scania, Muhammad Rifky Saputra; Scania, Dewi Ernita, Universitas Muslim Indonesia

Abstract

This community engagement program aims to empower the "Kana Tojeng" craftswomen group in Bontokassi Village, Galesong Selatan District, Takalar Regency, through training in e-commerce-based marketing of woven products made from lontara fiber. The primary issues faced by the partners include limited understanding of digital marketing, lack of an established online business identity, and underutilization of e-commerce platforms. The method applied was an educational-participatory approach, consisting of observation, awareness sessions, hands-on training, and evaluation. The results show a significant improvement in participants' knowledge and skills, with pre-test scores at 50.30% and post-test scores reaching 86.19%. The program also resulted in the activation of e-commerce accounts and enhanced digital marketing capabilities among local artisans. Supporting factors included high participant enthusiasm and contextual learning materials, while limitations in digital infrastructure emerged as the main challenge. This initiative demonstrates that integrating information technology into local business management is an effective strategy for enhancing the competitiveness of culturally rooted products.

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Mandasari, Juita; Indra, Dolly; Gafar, Andi Widya Mufila, Universitas Muslim Indonesia, Makassar, Indonesia

Abstract

Tanaman seledri merupakan komoditas hortikultura bernilai tinggi yang membutuhkan kelembapan tanah ideal antara 65%–75% untuk pertumbuhan optimal. Namun, proses penyiraman secara manual sering kali tidak konsisten dan bergantung pada kehadiran petani. Pemantauan kondisi tanah merupakan hal yang penting dalam melakukan penyiraman tanaman dengan kadar air yang sesuai. Penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem penyiraman otomatis berbasis Arduino Uno dan komunikasi LoRa untuk budidaya seledri skala kecil. Sistem ini memanfaatkan sensor soil moisture untuk mendeteksi tingkat kelembapan tanah, kemudian mengaktifkan pompa air secara otomatis melalui relay jika kelembapan berada di bawah ambang batas. Modul LoRa digunakan sebagai media komunikasi jarak jauh antara unit transmitter dan receiver, sementara LCD 16x2 menampilkan data kelembapan dan status pompa secara real-time. Sistem ini dirancang untuk melakukan penyiraman otomatis dan komunikasi LoRa yang dapat memantau kondisi tanah dan melakukan penyiraman secara mandiri sesuai dengan kebutuhan tanaman. Hasil pengujian simulator menunjukkan bahwa seluruh komponen bekerja dengan baik

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Hayati; Lilis Nur, Department of Electrical Engineering and Informatics, Universitas Negeri Malang, Malang, Indonesia; Department of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia; Handayani, Anik Nur; Irianto, Wahyu Sakti Gunawan, Department of Electrical Engineering and Informatics, Universitas Negeri Malang, Malang, Indonesia; Asmara, Rosa Andrie, Information Technology Department, Politeknik Negeri Malang, Malang, Indonesia; Indra, Dolly, Department of Computer Science, Universitas Muslim Indonesia, Makassar, Indonesia; Damanhuri, Nor Salwa, Electrical Engineering Studies, Universiti Teknologi MARA (UiTM),Cawangan Pulau Pinang, Malaysia

Abstract

Sign language recognition plays a critical role in promoting inclusive education, particularly for deaf children in Indonesia. However, many existing systems struggle with real-time performance and sensitivity to lighting variations, limiting their applicability in real-world settings. This study addresses these issues by optimizing a BISINDO (Bahasa Isyarat Indonesia) alphabet recognition system using the SSD MobileNetV2 architecture, enhanced with gamma correction as a luminance normalization technique. The research contribution is the integration of gamma correction preprocessing with SSD MobileNetV2, tailored for BISINDO and implemented on a low-cost assistive robot platform. This approach aims to improve robustness under diverse lighting conditions while maintaining real-time capability without the use of specialized sensors or wearables. The proposed method involves data collection, image augmentation, gamma correction (γ = 1.2, 1.5, and 2.0), and training using the SSD MobileNetV2 FPNLite 320x320 model. The dataset consists of 1,820 original images expanded to 5,096 via augmentation, with 26 BISINDO alphabet classes. The system was evaluated under indoor and outdoor conditions. Experimental results showed significant improvements with gamma correction. Indoor accuracy increased from 94.47% to 97.33%, precision from 91.30% to 95.23%, and recall from 97.87% to 99.57%. Outdoor accuracy improved from 93.80% to 97.30%, with precision rising from 90.33% to 94.73%, and recall reaching 100%. In conclusion, the proposed system offers a reliable, real-time solution for BISINDO recognition in low-resource educational environments. Future work includes the recognition of two-handed gestures and integration with natural language processing for enhanced contextual understanding.

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