Analisa Komparasi Algoritma Naïve Bayes, Decision Tree Dan KKN Untuk Klasifikasi Kebakaran Hutan Pada Wilayah Aljazair - Muhammad Fadhiil Alamsyah; Tri Putra Satriawan; Femmy Novica Ramadanis; Rahma Anugrah Mulyawan; Candra Edmond; Ricky Firmansyah (Jurnal Sistem Informasi dan Ilmu Komputer, 2023)
The Mediterranean region, in particular Algeria, is experiencing serious challenges due to the increased opportunities for forest fires. Since the mid-1970s, there has been a 50% reduction in rainfall over northwestern Algeria, making northern Algeria particularly vulnerable to the problem for many years. More than 37,000 hectares of sensitive forest are lost every year due to this extreme drought. The findings of this study, which assessed the hazard of forest fires from 2006 to 2019, agree with those of Bentchakal,Chibane (2022), who examined the problems caused by forest fires in the region. The aim of this investigation is to gain a better understanding of the problems caused by local forest fires and to use that expertise to provide insight for the authors and readers of this report. The report was written by presenting the findings of observations made using the Rapid Miner classification approach, which includes the categorization of areas affected by forest fires. Data is collected using a variety of algorithmic techniques, including Naive Bayes, KNN, and decision trees, which are used as tests of data to identify the most accurate results. The findings show that the Decision Tree technique has the best accuracy of 86.49% and provides a thorough explanation of the data.