Comparative Analysis of Earthquake Prediction with SVM, Naïve Bayes, and K-Means Models: Comparative Analysis of Earthquake Prediction with SVM, Naïve Bayes, and K-Means Models - Muttaqin, Ahmad Fadhiil; Sunge, Aswan Supriyadi; Zy, Ahmad Turmudi (Information Technology and Science (ITScience), 2025)
Earthquakes are natural disasters with significant impacts on people and the environment, so effective methods for prediction are needed to improve preparedness and risk mitigation. This study analyzes the performance of three algorithms Support Vector Machine (SVM), Naïve Bayes, and K-Means in predicting earthquakes in Indonesia using a dataset containing 4,645 historical data from BMKG processed through preprocessing, data separation, analysis, and performance evaluation with RapidMiner tools. The results show that SVM has the best performance with 99.87% accuracy, 99.83% precision, and 95.61% recall, making it highly relevant for earthquake prediction. Naïve Bayes achieved 90.31% accuracy and 95.08% recall, but the low precision (57.24%) shows the limitations of this model. K-Means successfully clusters earthquakes into two categories: small (3,661 data) and large (55 data) earthquakes, with a Davies-Bouldin Index value of 0.579, reflecting good clustering quality. Based on these results, SVM is recommended as a superior earthquake prediction model, while Naïve Bayes and K-Means are more suitable for additional analysis. This approach confirms the potential of machine learning algorithms in supporting future earthquake risk mitigation.