Klasifikasi Gempa Bumi Berdasarkan Magnitudo Menggunakan Metode Logistic Regression - Mar’atuzzulfa, Salma; Prathivi, Rastri; Susanto, S (LPPM STIKOM Tunas Bangsa, 2025)
The purpose of this study is to categorize areas in Indonesia that are potentially prone to earthquakes using the logistic regression algorithm. Variables such as latitude, longitude, depth, and magnitude are used to analyze 118 data points of natural disasters that occurred in Indonesia in 2023. As much as 40% of the data is used for testing, while 60% is used for training. The magnitudes are high, medium, and low. The logistic regression method is used to determine the level of health in the area and assess the relationship between variables. The study's findings indicate that the model has an accuracy of 93.62%, precision of 94%, recall of 93%, and F1 skor of 93% overall. In addition, the evaluation of the model's kinerja using the confusion matrix indicates that algorithms might associate a given category with a high sensitivity to error. By identifying data points and creating Logistic regression can assist in developing more effective bencana mitigation strategies by identifying data points and producing accurate predictions. As a result, it is believed that the general public can reduce the amount of dampak gempa bumi.