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All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Jurnal Buana Informatika Jurnal Transformatika Proceeding of the Electrical Engineering Computer Science and Informatics JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) SemanTIK : Teknik Informasi Jurnal CoreIT IT JOURNAL RESEARCH AND DEVELOPMENT Indonesian Journal of Artificial Intelligence and Data Mining Techne : Jurnal Ilmiah Elektroteknika JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Compiler MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Sistem Cerdas Applied Technology and Computing Science Journal JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Teknologi Informasi dan Terapan (J-TIT) International Journal of Informatics and Computation Aviation Electronics, Information Technology, Telecommunications, Electricals, Controls (AVITEC) Jurnal Informatika dan Rekayasa Perangkat Lunak Respati Letters in Information Technology Education (LITE) Jurnal Teknik Informatika (JUTIF) Teknika Jurnal Computer Science and Information Technology (CoSciTech) Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (Senastindo) Jurnal Ilmu Komputer dan Teknologi (IKOMTI) Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) International Journal of Informatics Engineering and Computing
Mohammad Diqi
Universitas Respati Yogyakarta
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering Library & Information Science Mechanical Engineering Social Sciences Other

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GENERATIVE ADVERSARIAL NETWORKS FOR ANTERIOR CRUCIATE LIGAMENT INJURY DETECTION Mulyani, Sri Hasta; Diqi, Mohammad; Salsabil, Husna Arwa
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 1 (2024): JUTIF Volume 5, Number 1, February 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.1.1150

Abstract

This research explores the application of Generative Adversarial Networks (GANs) for detecting and classifying Anterior Cruciate Ligament (ACL) injuries using MRI images. The study utilized a dataset of 917 MRI images, each labeled as healthy, partially injured, or completely ruptured, to train the model. The performance of the GAN model was evaluated using a confusion matrix and a classification report, yielding an overall accuracy of 92%. The model demonstrated high proficiency in identifying healthy ACLs and partially injured ACLs but encountered some challenges in accurately identifying completely ruptured ACLs. Despite this, the results suggest that machine learning techniques, particularly GANs, have significant potential for enhancing the accuracy and efficiency of ACL injury detection. The ability of the model to distinguish between different degrees of injury could potentially aid in treatment planning. However, the study also underscores the need for further refinement of the model, particularly in improving its sensitivity in detecting severe ACL injuries. This research highlights the potential of machine learning in medical imaging and provides a solid foundation for future research in ACL injury detection and classification.
Machine Learning for Environmental Health: Optimizing ConcaveLSTM for Air Quality Prediction Diqi, Mohammad; Hamzah; Ordiyasa, I Wayan; Wijaya, Nurhadi; Martin, Benedicto Reynaka Filio
Jurnal Buana Informatika Vol. 15 No. 01 (2024): Jurnal Buana Informatika, Volume 15, Nomor 01, April 2024
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v15i1.8707

Abstract

This study investigates the optimization of the ConcaveLSTM model for air quality prediction, focusing on the interplay between input sequence lengths and the number of LSTM units to enhance forecasting accuracy. Through the evaluation of various model configurations against performance metrics such as RMSE, MAE, MAPE, and R-squared, an optimal setup featuring 50 input steps and 300 neurons was identified, demonstrating superior predictive capabilities. The findings underscore the critical role of model parameter tuning in capturing temporal dependencies within environmental data. Despite limitations related to dataset representativeness and environmental variability, the research provides a solid foundation for future advancements in predictive environmental modeling. Recommendations include expanding dataset diversity, exploring hybrid models, and implementing real-time data integration to improve model generalizability and applicability in real-world scenarios.
Enhancing Hepatitis Patient Survival Detection: A Comparative Study of CNN and Traditional Machine Learning Algorithms DIQI, MOHAMMAD; HISWATI, MARSELINA ENDAH; DAMAYANTI, EKA
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 10, No 1 (2024): June 2024
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v10i1.28241

Abstract

Hepatitis patient survival prediction is a critical medical task impacting timely interventions and healthcare resource allocation. This study addresses this issue by exploring the application of a Convolutional Neural Network (CNN) and comparing it with traditional machine learning algorithms, including Support Vector Machine (SVM), Decision Tree, k-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), and Gradient Boosting (GBoost). The research objectives include evaluating the algorithms' performance regarding confusion matrix metrics and classification reports, aiming to achieve accurate predictions for both "Live" and "Die" categories. The dataset of 155 instances with 20 features underwent preprocessing, including data cleansing, feature conversion, and normalization. The CNN model achieved perfect accuracy in hepatitis patient survival prediction, outperforming the baseline algorithms, which exhibited varying accuracy and sensitivity. These findings underscore the potential of advanced machine learning techniques, particularly CNNs, in improving diagnostic accuracy in hepatology.
Leveraging Vector Quantized Variational Autoencoder for Accurate Synthetic Data Generation in Multivariate Time Series Mohammad Diqi; Ema Utami; Kusrini Kusrini; Ferry Wahyu Wibowo
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i3.4514

Abstract

This study addresses the challenge of generating high-quality synthetic financial time series data, acritical issue in financial forecasting due to limited access to complete and reliable historical datasets.The aim of this research was to compare the performance of the standard Variational Autoencoder andthe Vector Quantized Variational Autoencoder (VQ-VAE) in generating synthetic multivariate time seriesdata using the Adaro Energy Indonesia stock dataset. The VQ-VAE incorporates a discrete latentspace to improve the structure and control of the data generation process, whereas the standard VAEutilizes a continuous latent space. This research method was based on the implementation of bothmodels, followed by a quantitative evaluation using statistical metrics, including mean absolute error(MAE), mean squared error (MSE), root mean squared error (RMSE), and R² score. This researchshowed that the VQ-VAE outperformed the standard VAE in replicating the statistical characteristicsof stock prices, as shown by lower error values and higher R² scores across all tested features. The discretelatent space of the VQ-VAE led to the generation of more structured and statistically consistentsynthetic data. The implications of these findings suggest that the VQ-VAE model is highly suitablefor financial forecasting applications and indicate the potential for future enhancements throughintegration with hybrid models, such as attention mechanisms or generative adversarial networks.
Adaptive Kernel Probability Model (AKPM) for Interpretable and Reliable Diabetes Prediction using Clinical Diagnostic Data Hiswati, Marselina Endah; Azijah, Izattul; Subandi, Yeyen; Diqi, Mohammad
Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Vol 8, No 1 (2026): February
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/avitec.v8i1.3689

Abstract

Diabetes mellitus poses a growing global health concern, particularly in low- and middle-income countries where early detection remains limited, demanding classification models that balance accuracy, interpretability, and adaptability to heterogeneous clinical data. This study proposes and evaluates the Adaptive Kernel Probability Model (AKPM), a novel nonparametric probabilistic classifier designed to enhance diabetes prediction by performing localized kernel density estimation with adaptive bandwidth selection via k-nearest neighbors. Implemented and tested on the Pima Indians Diabetes Dataset, AKPM outperformed conventional classifiers—Naïve Bayes and Gaussian Mixture Models (GMM)—across all evaluation metrics, achieving 87.5% accuracy, 83.3% precision, 76.9% recall, and an F1-score of 80.0% for the diabetic class, alongside 89.3% precision and 92.6% recall for the normal class. These results surpassed GMM (83.0% accuracy, 71.6% F1-score) and Naïve Bayes (80.0% accuracy, 66.6% F1-score), confirming AKPM’s superior capability to detect diabetic cases while minimizing false negatives. Offering transparent posterior inference and a modular design, AKPM emerges as a reliable and interpretable solution for clinical decision support systems and real-world healthcare applications.
EBAQ: An Entropy-Based Bit Allocation Framework for Lightweight Autoencoder Models Zaidir, Zaidir; Mohammad Diqi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 1 (2026): February 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i1.6925

Abstract

Autoencoder-based models have shown strong potential for anomaly detection in complex time-series data; however, they often assume equal importance across latent dimensions, resulting in inefficiencies and reduced precision. This study addresses this limitation by introducing the Entropy-Based Bit Allocation Quantizer (EBAQ), a novel quantization framework that adaptively allocates bits to each latent dimension based on its entropy, preserving more precision where information content is highest. The primary objective is to enhance representational efficiency and anomaly detection performance without increasing model complexity or computational cost. EBAQ is implemented as a plug-and-play module within a standard autoencoder architecture, requiring no retraining or architectural modification. The method was evaluated using a publicly available ECG dataset, where reconstruction-based anomaly detection was employed to assess its performance. Results show that EBAQ outperforms the standard autoencoder baseline, achieving higher accuracy (94.9%), precision (99.4%), and recall (91.4%), while also demonstrating more apparent separation between normal and anomalous data in latent space visualizations. These findings confirm that entropy-aware quantization improves both fidelity and interpretability in unsupervised anomaly detection. Overall, this work presents a theoretically grounded and practically efficient solution that bridges information theory and deep learning, offering a human-centered approach to developing more intelligent and efficient AI systems for real-world applications.
Co-Authors Ahmad Wakhid Ariani, Rahayu Cahya Arum Kurnia Sulistyawati Arum Kurnia Sulistyowati Azijah, Izattul Bagus Subekti Nuswantoro Basuki, Umar Dian Rhesa Rahmayanti Dyan Avando Meliala Eka Damayanti Elisabeth Deta Lustiyati Ema Utami Erizal Erizal Farida Nur Aini Fathinah, Azzah FAUZIYYAH, ANNUR Ferry Wahyu Wibowo HAFIZAH, IDA Hamzah Hamzah Hamzah Husna Arwa Salsabil I Wayan Ordiyasa Ikhwan Mustiadi Indra Listiawan Jelina Imlabla Kristian, Tadem Vergi Kusrini Kusrini Lestari, Sri Listiawan, Indra Marselina Endah Hiswati Martin, Benedicto Reynaka Filio Meliala, Dyan Avando Mujastia Muhdalifah Noverianus, Noverianus nur aini, farida Ordiyasa, I Wayan Putra Wanda Putra Wanda, Putra Romana Herlinda Sahal, Ahmad Salsabela, Marcella Sri Hasta Mulyani Sri Lestari Sriwidodo B, Sriwidodo Subandi, Yeyen Sugeng Winardi Suwarto Suwarto Syafitri, Endang Nurul Syihab, Namira Anjani Rahmadina Wandani, Aulia Fadillah Wani Widyaningrum, Rini Wijaya, Nurhadi Wiratma, Harits Dwi Zaidir