<![CDATA[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]]>
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