<![CDATA[Abstrak: Salah satu bencana alam yang sering terjadi di Indonesia adalah kebakaran. Pada tahun 2019, terdapat 1.124 titik panas di Kalimantan Barat. Kebakaran hutan menyebabkan banyak kerugian, termasuk deforestasi hutan dan hasilnya, pencemaran kabut asap dan emisi dan turunnya kualitas udara, penelitian ini menerapkan algoritma Naive Bayes Classifier untuk menentukan tingkat status rawan kebakaran hutan dan lahan di Kabupaten Kubu Raya. Hasil akurasi klasifikasi status kebakaran hutan dan lahan dengan algoritma Naïve Bayes 92% pada data latih dan 96% pada data uji. Dapat ditarik kesimpulan bahwa Gaussian Naïve Bayes dapat mengklasifikasi status low, medium, high pada kebakaran hutan dan lahan dengan baik.Abstract: One of the most recent natural disasters in Indonesia is wildfires. In 2019, there were 1,124 hotspots in West Kalimantan. Forest fires cause extensive damage, including reducing forest area and yields, air pollution from haze and emissions, decreasing agricultural potential, and overall forest depletion. The aim of this research is to apply the Naive Bayes Classifier algorithm to determine the level of forest and land fire vulnerability status in Kubu Raya District and assess the accuracy of the Naïve Bayes Classifier method in classifying forest and land fire status. The method employed in this study is Gaussian Naïve Bayes. The classification accuracy of forest and land fire status using Naive Bayes algorithm is 92% on training data and 96% on test data. It can be concluded that Gaussian Naïve Bayes effectively classifies forest and land fire status]]>
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