Implementasi Algoritma Random Forest untuk Mengklasifikasikan Data Gempa Bumi di Indonesia - Pratiwi, Alda Putri; Arum, Prizka Rismawati (Eigen Mathematics Journal, 2025)
Earthquakes are shocks that occur on the surface of the earth due to shifts in the earth's plates. Geographically, Indonesia is located in the Pacific Ring of Fire (King of Fire) region, this makes Indonesia prone to earthquakes. Earthquakes can cause environmental damage and tsunami disasters, but not all earthquakes can cause tsunamis. Classifying earthquakes that have the potential for a tsunami is very important to mitigate the damage caused. One classification method that has a high level of accuracy is random forest. The advantage of random forest is that this algorithm tends to be resistant to overfitting and can handle large data. This research uses real-time earthquake data from July to August 2023, sourced from the website of the Meteorology Climatology and Geophysics Agency (BMKG). The training data and test data used in this research are 70% and 30%. Confution Matrix is used as model evaluation, to measure the accuracy of the classification model. The results of this research obtained a high accuracy, equal 0.97 or 97%.