Comparative Performance Analysis of Random Forest and Multilayer Perceptron Algorithms for Earthquake Magnitude Prediction in Indonesia

Indonesia is situated within the Pacific Ring of Fire, making it highly susceptible to frequent seismic activities. High-magnitude earthquakes can result in substantial damage to communities, and earthquake magnitude prediction remains a highly complex field. However, with advancing technology, numerous studies have been conducted to predict earthquake magnitudes, particularly in Indonesia. Through the implementation of machine learning, several algorithmic models have demonstrated potential in earthquake prediction, specifically Random Forest and Multilayer Perceptron (MLP). This research aims to analyze the comparative performance of Random Forest and Multilayer Perceptron algorithms, along with the implementation of hyperparameter tuning for earthquake magnitude prediction in Indonesia. The study seeks to identify the most accurate algorithmic model for predicting earthquake magnitudes in Indonesia and to determine whether hyperparameter tuning enhances the performance of Random Forest and MLP algorithms. The features utilized in this study include latitude, longitude, depth, magnitude, and Julian’s day. Earthquake data was obtained from the United States Geological Survey (USGS) Earthquake website. Through the application of these features and hyperparameter tuning, this research identified Random Forest as the optimal model for earthquake magnitude prediction in Indonesia, achieving evaluation metrics of MSE at 0.1488, MAE at 0.2832, MAPE at 6.2154, and RMSE at 0.1384.