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All Journal Jurnal Pendidikan dan Konseling Journal of Information Systems and Informatics Innovative: Journal Of Social Science Research GURUPEDIA: Journal of Teacher and Education
Mz, Muhammad Amanulloh
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Humanities Computer Science & IT Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Public Health Other

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Journal : Journal of Information Systems and Informatics

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Performance Comparison of Random Forest, XGBoost, and SVM for Flood Risk Prediction Using BNPB GIS Data Mz, Muhammad Amanulloh; Nurhayati, Oky Dwi; Suseno, Jatmiko Endro
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.
Co-Authors Arini Arini Jatmiko Endro Suseno Muhammad Zidni Ilman Nafiah Nur Azizah Nur Ismianah Oky Dwi Nurhayati Refianto, Eddy Rohmatin, Binti Laila Rohmatin, Binti Lailatur Sugianto Sugianto Wibowo, Ericha Lisa