Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Bulletin of Artificial Intelligence
Farizi Ilham
Universitas Pamulang, Tangerang Selatan
Computer Science & IT Control & Systems Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Prediksi Magnitudo Gempa Indonesia Menggunakan Machine Learning Berbasis Data Seismik Farizi Ilham; Halili Maar; Dimas Lendesi
Bulletin of Artificial Intelligence Vol 5 No 1 (2026): April 2026
Publisher : Graha Mitra Edukasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62866/buai.v5i1.226

Abstract

Indonesia is one of the world's most seismically active regions, making earthquake magnitude prediction an important component of disaster mitigation. This study compares several machine learning regression algorithms for predicting earthquake magnitude using Indonesian seismic data collected from the United States Geological Survey (USGS) during 2016–2023. The dataset contains 17,331 earthquake records consisting of spatial, measurement-quality, and temporal attributes. The research process included data preprocessing, feature engineering, model training, and evaluation using the Coefficient of Determination (R²), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). Four regression algorithms were evaluated: Linear Regression, Support Vector Regression, Random Forest Regression, and Gradient Boosting Regression. Experimental results show that Random Forest Regression achieved the best performance with an R² of 0.767, MAE of 0.123, and RMSE of 0.179, followed by Gradient Boosting Regression with an R² of 0.746. Feature importance analysis indicates that magError, depthError, depth, and gap are the most influential variables in earthquake magnitude prediction. These findings demonstrate that ensemble learning provides accurate prediction performance while offering meaningful interpretation of influential seismic parameters for disaster mitigation systems
Co-Authors Dimas Lendesi Halili Maar