Pengaruh Optimasi Hyperparameter Random Forest terhadap Akurasi Prediksi Magnitudo Gempa Bumi Berdasarkan Hasil Klasterisasi DBSCAN
DOI:
https://doi.org/10.35870/jtik.v10i1.5555Keywords:
Earthquake, DBSCAN, Random Forest, Magnitude Prediction, Machine Learning, Hyperparameter TuningAbstract
Indonesia is a country with high seismic activity due to its location at the convergence of three major tectonic plates. This condition creates a strong need for earthquake pattern analysis and magnitude prediction to support disaster mitigation. This study aims to cluster earthquake data using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and to predict earthquake magnitude using the Random Forest algorithm optimized through hyperparameter tuning. The Indonesian earthquake dataset was obtained from Kaggle with a total of 92,887 valid entries. The DBSCAN clustering results revealed several active seismic zones, particularly in Sumatra, Java, Sulawesi, and Papua. The comparison of R² between the Baseline Random Forest and the Tuned Random Forest shows a significant improvement after the parameter tuning process. The Tuned Random Forest model achieves an R² value of 0.478, which is higher than the Baseline Random Forest's 0.442. This indicates that the tuned model is better able to explain the variance in the data and provides more accurate predictions.
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