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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Journal of Development Research Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Syntax Literate: Jurnal Ilmiah Indonesia Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Journal of Information Systems and Informatics Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Indonesia Sosial Teknologi
Moch Arief Soeleman
Dian Nuswantoro University
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Social Sciences Other

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ANALISIS ALGORITMA MACHINE LEARNING DENGAN TEKNIK SMOTE UNTUK PENINGKATAN SENSITIVITAS MODEL DETEKSI SINDROM OVARIUM POLIKISTIK (PCOS) Mamay Maida; M. Arief Soeleman; Hestiana Putri Novitasari; Sifa Ayu Rosita Sari
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7203

Abstract

Polycystic Ovary Syndrome (PCOS) is an endocrine disorder affecting approximately 6–21% of women of reproductive age but is often difficult to detect in its early stages. This study develops a PCOS detection model using machine learning and the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance between positive and negative classes. Four algorithms were tested: Logistic Regression (LR), K-Nearest Neighbors (KNN), Decision Tree (DT), and Random Forest (RF). The dataset used was obtained from Kaggle and underwent preprocessing, including data cleaning, encoding, and feature selection based on correlation. After balancing the data using SMOTE, standardization was applied to ensure feature scale consistency.Model evaluation was carried out using a confusion matrix as the basis for calculating Accuracy, Precision, Recall, F1-score, Specificity, and Negative Predictive Value (NPV), with Recall as the primary focus. The results show that LR achieved the highest Recall (0.92), while RF demonstrated the best performance balance with Accuracy (0.92), F1-score (0.87), Specificity (0.95), and NPV (0.93). KNN and DT obtained the same Recall value (0.83), although their Precision and F1-score were slightly lower. In addition, a before–after analysis was conducted to evaluate the effect of SMOTE, and the McNemar test was used to assess the statistical significance of performance differences between models. Feature Importance analysis revealed that follicle count, menstrual cycle length, and fast-food consumption patterns are the most influential factors contributing to PCOS risk. These findings indicate that the application of SMOTE significantly enhances model sensitivity and has strong potential to be developed as a decision support system in reproductive health.
Adaptive threshold for moving objects detection using gaussian mixture model Moch Arief Soeleman; Aris Nurhindarto; Muslih Muslih; Karis W.; Muljono Muljono; Farikh Al Zami; R. Anggi Pramunendar
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 2: April 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i2.14878

Abstract

Moving object detection becomes the important task in the video surveilance system. Defining the threshold automatically is challenging to differentiate the moving object from the background within a video. This study proposes gaussian mixture model (GMM) as a threshold strategy in moving object detection. The performance of the proposed method is compared to the Otsu algorithm and gray threshold as the baseline method using mean square error (MSE) and Peak Signal Noise Ratio (PSNR). The performance comparison of the methods is evaluated on human video dataset. The average result of MSE value GMM is 257.18, Otsu is 595.36 and Gray is 645.39, so the MSE value is lower than Otsu and Gray threshold. The average result of PSNR value GMM is 24.71, Otsu is 20.66 and Gray is 19.35, so the PSNR value is higher than Otsu and Gray threshold. The performance of the proposed method outperforms the baseline method in term of error detection.
Comparative Analysis of ResNet-Based Wagner-Scale Classification for Imbalanced DFU Data Aditya Wahyu Ramadhan; Pulung Nurtantio Andono; M. Arief Soeleman
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.7016

Abstract

Diabetic Foot Ulcers (DFU) are a serious complication of diabetes mellitus and carry a high risk of lower extremity amputation if not treated in a timely manner. The conventional classification process, which relies on visual inspection by clinicians, tends to be subjective and inconsistent. Therefore, this study proposes a multiclass classification model for DFU based on the Wagner Scale (Grades 0–5) using the ResNet-50 architecture with a transfer learning approach as the core machine learning method. The dataset used in this study consists of 1,415 clinical wound images that were annotated and verified by medical professionals. The dataset is highly imbalanced, with 543 images in Grade 0, 110 in Grade 1, 252 in Grade 2, 145 in Grade 3, 293 in Grade 4, and only 72 images in Grade 5. To address this imbalance, random oversampling (ROS) was applied, in addition to standard preprocessing techniques such as normalization and data augmentation to increase training data diversity.Experimental results demonstrate that the proposed model achieves high classification performance based on accuracy, precision, recall, and F1-score. Specifically, the model obtained a precision of 0.96, recall of 0.95, and F1-score of 0.95, indicating consistent and robust classification performance across all Wagner grades. The best configuration (ResNet-50 + ROS) successfully improved the classification performance across minority grades (e.g., Grade 1 and Grade 5). Moreover, the model consistently identifies minority classes and does not exhibit signs of overfitting. Model optimization using the Adam optimizer and data balancing strategies significantly improves the generalization capability of the classifier. These findings indicate that the proposed model is not only effective for automatic DFU classification, but also has great potential to support objective clinical decision making and accelerate diagnosis, particularly in healthcare facilities with limited resources.
Co-Authors Abdul Syukur Abi Senoprabowo Adhe Irham Thoriq Aditya Wahyu Ramadhan Affandy Affandy Ahmad Zainul Fanani Ajib Susanto Ali Muqoddas Alzami, Farrikh Anas Syaifudin Anas Syaifudin Ari Yahya A Arry Maulana Syarif Dwi Puji Prabowo, Dwi Puji Edi Noersasongko Edi Sugiarto Elkaf Rahmawan Pramudya Esa Wahyu Andriansyah Farikh Al Zami Fikri Budiman Fikri Budiman Heribertus Himawan Hestiana Putri Novitasari Imam Bagus Pratama Karis W. Khafiidz Hastuti Kristhina Evandari Lu'luul Maknun Magus Sarasnomo Mamay Maida Mohammad Ahmad Maidanul Abrori Mohammad Faishol Zuhri Much Chafid Muhammad Haris Zuhri Muhammad Munsarif Mula Agung Barata Muljono, - Mursalim Mursalim Muslih Muslih Muslih Muslih Nanang Lestio Wibowo Noor Ageng Setiyanto, Noor Ageng Nurhindarto, Aris Pujiono Pujiono Pulung Nurtantio Andono Puri Sulistiyawati Purwanto Purwanto Ricardus Anggi Pramunendar S. Kholidah Rahayu Maharani Saiful Ulya Sifa Ayu Rosita Sari Supriyono Asfawi Via Indriani Kumalasari Widyatmoko Karis