Research Publications

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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Authors

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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Authors

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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Authors

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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Authors

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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Authors

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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Authors

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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