Pemetaan Kerawanan Kebakaran Hutan dan Lahan Berbasis Hybrid AHP-RF dan NASA VIIRS-FIRMS di Kalimantan
Abstract
Forest and land fires are recurrent disasters in Kalimantan that have significant impacts on the environment, public health, and the economy. This study develops a hybrid model that combines Analytic Hierarchy Process (AHP) and Random Forest weighting using four criteria: rainfall, peatland, hotspot history, and distance to settlements. The final model assigns the highest weight to hotspot history (43.0%) as the strongest predictor. Four districts/cities, namely Ketapang, Pulang Pisau, Kubu Raya, and Palangka Raya, are classified as high risk (7.3% of the total evaluated districts/cities), while 26 districts/cities (47.3%) are classified as medium risk and 25 (45.5%) as low risk. Validation against 15,272 independent hotspot observations demonstrates a strong Pearson correlation (r = 0.718), a moderate Spearman correlation (ρ = 0.573), and significant differences among risk classes based on one-way ANOVA (F = 27.724, p < 0.0001). A ±20% weight sensitivity analysis indicates that the model is highly robust, while classification accuracy yields a Cohen’s Kappa of 0.288. Overall, the hybrid AHP–Random Forest model provides adequate validity to support decision-making in forest and land fire mitigation.
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DOIAuthor Biographies
Program Studi Teknologi Informasi, Direktorat Kampus Jakarta, Universitas Telkom, Kota Jakarta Barat, Daerah Khusus Ibukota Jakarta, Indonesia.
Program Studi Sistem Informasi, Universitas Telkom, Kota Jakarta Barat, Daerah Khusus Ibukota Jakarta, Indonesia.
Program Studi Teknik Telekomunikasi, Universitas Telkom, Kota Jakarta Barat, Daerah Khusus Ibukota Jakarta, Indonesia.
Program Studi Teknologi Informasi, Direktorat Kampus Jakarta, Universitas Telkom, Kota Jakarta Barat, Daerah Khusus Ibukota Jakarta, Indonesia.
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