PREDIKSI RISIKO KEBAKARAN MENGGUNAKAN ALGORITMA NAÏVE BAYES BERDASARKAN DATA HISTORIS DAN LINGKUNGAN - Ismanizan, Ryan; Fitri, Triyani Arita; Rahmiati, Rahmiati; Agustin, Agustin (Jurnal Teknik Informasi dan Komputer (Tekinkom), 2025)
Fire is a disaster that can cause significant material and human losses. Kampar Regency in Indonesia is a fire-prone area due to short circuits, human negligence, and environmental conditions. This study aims to predict fire risk based on historical fire incident data and environmental factors using the Naïve Bayes algorithm. This method was chosen because of its ability to classify large-dimensional data with high probability. The research stages include data collection, preprocessing, data exploration, modeling, and model evaluation. Data were tested using splits of 70:30, 80:20, and 90:10. The results showed that the Naïve Bayes algorithm was able to provide prediction accuracy levels of 95.82%, 96.00%, and 95.45%, respectively. The highest accuracy level was obtained in the 80:20 scenario. These findings indicate that Naïve Bayes is effective in predicting high-risk areas for fire and can serve as a reference for relevant parties in developing more targeted fire prevention and mitigation policies.