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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Pendidikan dan Konseling Journal of Information Systems and Informatics Innovative: Journal Of Social Science Research Journal of Innovative and Creativity GURUPEDIA: Journal of Teacher and Education NJCOM: Community Service Journal
Muhammad Amanulloh Mz
Institut Teknologi dan Bisnis PGRI Dewantara Jombang
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Education Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences Other

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Performance Comparison of Random Forest, XGBoost, and SVM for Flood Risk Prediction Using BNPB GIS Data Muhammad Amanulloh Mz; Oky Dwi Nurhayati; Jatmiko Endro Suseno
Journal of Information System and Informatics Vol 8 No 1 (2026): February
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i1.1461

Abstract

This study compares the performance of three machine learning algorithms—Random Forest, XGBoost, and Support Vector Machine (SVM)—for predicting flood risk using spatial and non-spatial data from BNPB GIS. The analysis focuses on disaster records from January 3 to 15, 2026, with district-city as the spatial unit of observation. Following data cleaning, exploratory analysis, and feature preparation, the models were evaluated using ROC-AUC, PR-AUC, F1-Score, Precision, Recall, and Accuracy. XGBoost demonstrated the highest ROC-AUC (0.675), indicating strong overall performance in distinguishing flood from non-flood events. Random Forest achieved the highest Recall (0.947), showing superior sensitivity in detecting flood events, while SVM exhibited fluctuating performance with a lower ROC-AUC (0.496). Visualizations of model behavior and spatial flood patterns were provided to support model interpretability. The study’s results suggest that ensemble models, particularly XGBoost and Random Forest, can significantly enhance flood risk prediction, improving the accuracy and sensitivity of early warning systems. These findings contribute to the development of more effective data-driven flood mitigation strategies in Indonesia, enabling better disaster preparedness and response.
Benchmarking Lightweight Machine Learning for Disaster Prediction: Accuracy, Latency, and Readiness Muhammad Amanulloh Mz; Oky Dwi Nurhayati; Jatmiko Endro Suseno
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7570

Abstract

Natural disaster early warning systems are often constrained by high computational latency, which impedes the timely dissemination of critical information. This study proposes a disaster impact prediction framework designed to optimize inference speed through a lightweight machine learning approach. Utilizing a comprehensive historical dataset from the Indonesian National Board for Disaster Management (BNPB) comprising 28,773 disaster records across 34 provinces from January 2018 to May 2024, this research evaluates the performance of XGBoost and LightGBM against a Gated Recurrent Unit (GRU) model for predicting infrastructure damage. SMOTE was applied exclusively during the training phase to address class imbalance without affecting inference speed. The results demonstrate that tree-based models significantly outperform GRU in both accuracy and speed. XGBoost achieved the lowest Mean Absolute Error (MAE of 1.1649) and the fastest inference latency (0.0026 ms per sample), followed by LightGBM (MAE of 1.4006, latency of 0.0064 ms), while GRU yielded a substantially higher error (MAE of 1.4189) and latency (0.2493 ms). Statistical validation via the Wilcoxon signed-rank test confirmed the significance of these performance differences (p < 0.001). Beyond technical benchmarking, these findings support the development of Intelligent Decision Support Systems for disaster mitigation, where organizational readiness and technology acceptance are critical for successful adoption. This study discusses practical implications for deploying lightweight models on resource-constrained edge computing devices such as Raspberry Pi 4, demonstrating sub-millisecond response speed, horizontal scalability, and affordability under $100 per node.
Co-Authors Alingka Fahada Firmatasya Arbiati Faizah Arini Arini Jatmiko Endro Suseno Jatmiko Endro Suseno M. Mafatihul Falah Mz Muhammad Zidni Ilman Nafiah Mz, Nur Ismianah Nur Azizah Nur Ismianah Nur Ismianah Oky Dwi Nurhayati Refianto, Eddy Rohmatin, Binti Laila Rohmatin, Binti Lailatur Sugianto Sugianto Wibowo, Ericha Lisa