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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Robotics and Automation (IJRA) IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Applied Sciences ComEngApp : Computer Engineering and Applications Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Al-Iqtishad : Jurnal Ilmu Ekonomi Syariah (Journal of Islamic Economics) JUSIFO : Jurnal Sistem Informasi Annual Research Seminar Proceeding of the Electrical Engineering Computer Science and Informatics Journal of Information Systems and Informatics SULUH: Jurnal Abdimas Indonesian Journal of Electrical Engineering and Computer Science Jurnal Generic Jurnal Abdimas Bina Bangsa Smart: Journal of Sharia, Tradition, and Modernity Malcom: Indonesian Journal of Machine Learning and Computer Science Muslim Business and Economic Review Journal of Artificial Intelligence and Digital Business Jurnal Ekonomi Bisnis, Manajemen dan Akuntansi
Bambang Tutuko
Sriwijaya University
Religion Automotive Engineering Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Physics Social Sciences Other

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Cluster-Based Random Forest Regression for Internet of Things Bandwidth Prediction Using Leakage-Free Features Hutagalung, Naek Parulian; Tutuko, Bambang; Zarkasi, Ahmad
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 3 (2026): MALCOM July 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i3.2894

Abstract

Heterogeneous Internet of Things (IoT) deployments generate highly variable network traffic, yet most existing bandwidth management approaches rely on static provisioning or device-agnostic models that fail to capture per-device traffic heterogeneity. This study proposes a hybrid machine learning framework combining K-Means clustering and Random Forest regression to enable cluster-aware, leakage-free bandwidth consumption prediction for IoT environments. Using 994,145 flow records from the UNSW HomeNet dataset, the Log1p transformation was applied to address heavy-tailed traffic distributions, and direct bandwidth-derived features were strictly excluded to prevent data leakage. Devices were segmented into three traffic profiles (Low, Medium, and High), validated by a Silhouette Score of 0.4153 and a Davies–Bouldin Index of 1.0357. Within the structural constraints of this single-dataset, controlled setting, Random Forest regression achieved R² values above 0.99 across all clusters, with stable cross-validated performance, substantially outperforming Linear Regression, which achieved R² values as low as 0.0374 in the Medium cluster. Feature importance analysis revealed that Inter-Arrival Time is the primary driver of bandwidth consumption, superseding raw payload volume, with direct implications for temporally-driven feature design. These results indicate that cluster-aware, leakage-free models offer a viable architecture for IoT bandwidth management, with cross-domain generalization as a primary direction for future work.
Fetal organ detection using feature enhancement with attention and residual block Nuswil Bernolian; Siti Nurmaini; Ade Iriani Sapitri; Annisa Darmawahyuni; Muhammad Naufal Rachmatullah; Bambang Tutuko; Firdaus Firdaus
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1593-1604

Abstract

The rapid advancements in fetal ultrasonography have significantly enhanced prenatal diagnosis in recent years. Deep learning (DL) architectures have further streamlined the process of organ detection, improved diagnostic accuracy, and reduced observer dependency. This study proposes a computer-aided DL approach for fetal organ segmentation using the you only look once (YOLO) algorithm, a state-of-the-art method for object detection and image segmentation. This study identified and classified 15 fetal organs, including the umbilical vein, stomach, abdomen, brain (trans-cerebellum, trans-thalamic, and trans-ventricular regions), femur, head, thorax (chest cavity), heart (circumference, left atrium, left ventricle, right atrium, right ventricle), and aorta. We compared the performance of YOLOv7, YOLOv8, YOLOv9, and YOLOv11 architectures. The results showed that YOLOv9 outperformed YOLOv7, YOLOv8, and YOLOv11 achieving mAP50 and mAP95 scores of 91.90% and 94.50%, respectively. This performance surpasses previous studies that focused on classifying only a limited number of fetal organs.
Delineating 12-lead ECG for automated ST-elevation and ST depression detection using deep learning Bambang Tutuko; Annisa Darmawahyuni; Alexander Edo Tondas; Muhammad Naufal Rachmatullah; Firdaus Firdaus; Ade Iriani Sapitri; Anggun Islami; Sukemi Sukemi; Muhammad Fachrurrozi; Siti Nurmaini; Rendy Isdwanta; Jordan Marcelino
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.10531

Abstract

ST-elevation or ST-depression are markers of an abnormal heart condition detected through an electrocardiogram (ECG) where the tracing in the ST-segment is unusually elevated above the TP-segment (baseline). Identifying the localization of the ST-segment on an ECG is difficult because even a minor change in the ST-segment can be obscured by filtering processes. The 12-lead ECG signal is a non-invasive tool in the early detection of ST-elevation based on ST- and TP-segment, with quick and accurate interpretation. This study proposes a standard 12-lead ECG delineation model using deep learning (DL). The ECG signal has been segmented to Pstart–Pend, Pend–QRSstart, QRSstart–Rpeak, Rpeak–QRSend, QRSend-Tstart, Tstart–Tend, and Tend–Pstart. The study interpreted ST-elevation or -depression using an ECG delineation approach guided by medical rules. The findings revealed that the DL model achieved an average accuracy of 99.18%, sensitivity of 92.55%,specificity of 99.55%, precision of 92.61%, and F1-score of 92.52% in limb leads. Similarly, in chest leads, the DL model attained an accuracy of 99.16%, sensitivity of 93.10%, specificity of 99.53%, precision of 93.32%, and F1-score of 93.11%. This study also validated the DL-predicted results by a cardiologist from Mohammad Hoesin Hospital, Indonesia.
Co-Authors Abied, Wildan Ade Iriani Sapitri Ade Iriani Sapitri Aditya Aditya Aditya, Aditya Agus Triadi Agus Triadi Ahmad Dahlan Malik Ahmad Hudaifah, Ahmad Ahmad Rifai Ahmad Rifai Ahmad Zarkasi Ahmad Zarkasi Ahmad Zarkasi Alexander Edo Tondas Ananda, Dea Agustria Anggun Islami Anik Kustirini, Anik Annisa Darmawahyuni Arum, Akhiar Wista Asyhad, M. Aulia Rahman Thoharsin Bayu Wijaya Putra Borisman Richardson Chatarina Umbul Wahyuni Cholilie, Irvan Adhin Darmawahyuni, Annisa Darmawahyuni, Annisa Dewi, Kemala Dimas Budianto Dwi, Galih Malik Faisal Fajri Fajri, Faisal Febrita, Suci Findayani, Vinka Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Ganesha Ogi Hanif Habibie Supriansyah Huda Ubaya Hutagalung, Naek Parulian Isdwanta, Rendy Islami, Anggun Ivall, Mochammad Jasmir Jasmir Jordan Marcelino Kemala Dewi Khairunnisa, Cholidah Zuhroh Kustyadji, Gatot M. Fachrurrozi . Maharani, Masayu Nadila Marcelino, Jordan Moh. Mukri Muhammad Afif Muhammad Fachrurrozi Muhammad Irham Rizki Fauzi Muhammad Naufal Rachmamtullah Muhammad Naufal Rachmatullah Nurnazli Nuswil Bernolian Pamela, Maylavalaza PATIYUS AGUSTIANSYAH, PATIYUS PP Aditya, PP, Aditya, PP Prasetya, Fandi Angga Pratama, Yogi Tiara Pudjihardjo, Hari Setijo Pudjihardjo, Hari Setijo Purwanto Purwanto Rachmamtullah, Muhammad Naufal Rendy Isdwanta Reza Firsandaya Malik Rizal Sanif Rizky, Anandhita Rossi Passarella Samsuryadi Samsuryadi Sapitri, Ade Iriani Saraswati, Ade Maya Sari, Ririn Purnama Sarifah Putri Raflesia Sarmayanta Sembiring Sastradinata, Irawan Setyati Budiningrum, Diah Siti Nurmaini Sukemi Sukemi Sutarno Sutarno Sutarno Sutarno Sutarno Sutrimo Sutrimo Tjiptohadi Sawarjuwono Tresna Dewi Triadi, Agus Velia Yuliza W, Prayogo Pandhu Wardhana, Aditya Narendra Winda Kurnia Sari