Research Publications

Authors

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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Authors

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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Authors

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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Authors

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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Authors

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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Authors

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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Authors

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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Authors

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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Authors

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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Authors

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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Authors

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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Authors

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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Authors

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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Authors

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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Authors

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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Authors

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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