KLASIFIKASI SEBARAN SPASIAL HOTSPOTS DI KALIMANTAN BARAT MENGGUNAKAN METODE SUPPORT VECTOR MACHINE - Naomi Nessyana Debataraja; Evi Nopita Sari; Joannes Fregis Philosovio Anugrahnu; Claudia Bella Hardiana (EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN, 2025)
Forest fires are a serious environmental issue that often leads to significant ecological and economic damage. In response, efforts have been made to predict and mitigate the impact of forest degradation by improving classification techniques. This study aims to use Support Vector machine (SVM) method to analyze the spatial distribution of forest fires in West Kalimantan. The model uses historical hotspot data as the dependent variable and considers various independent variables, including slope, topography, distances from rivers, settlements and roads, and land cover types. Due to the non-linear nature of the data, the SVM has been enhanced using the Gaussian Radial Basis Function (RBF) kernel. Optimal performance was achieved with cost (c) parameter values of 2, resulting in a classification accuracy of 75.86%. These findings suggest that, when supported by an appropriate kernel function, SVM is a viable and effective approach for mapping and analyzing forest fire-prone areas. The model provides a valuable decision-making tool for forest fire prevention and land management planning in regions with similar environmental characteristics.