Klasifikasi Risiko Bencana di Indonesia Menggunakan SVM dan Random Forest - Erland Adhe Sharendra; Tri Widodo; Damayanti Damayanti; Okma Arnilia (Forum Kerjasama Pendidikan Tinggi, 2026)
Indonesia is a country with a high level of disaster vulnerability, requiring effective methods to accurately classify disaster risk levels. This study aims to analyze and compare the performance of Support Vector Machine (SVM) and Random Forest algorithms in disaster risk classification. The dataset used consists of disaster event data from 2019–2024, including disaster type, region, number of victims, and population density. Disaster risk levels were classified into three categories, namely low, medium, and high, based on the total impact calculated from the number of victims. The proposed method includes data preprocessing, normalization, and train-test data splitting. The results show that both models achieved high performance, where Random Forest obtained an accuracy of 95.66% and SVM achieved 95.28%, with ROC-AUC values of 0.9823 and 0.9769, respectively. Random Forest demonstrated slightly better performance with an accuracy difference of 0.38% and more consistent prediction results. The high performance indicates that the models were able to recognize the main patterns within the dataset, although the results were also influenced by the characteristics of the data used. Overall, Random Forest is more suitable for disaster risk classification on data with complex characteristics.