<![CDATA[In September 2019, Riau was severely affected by hazardous haze, impacting the health of the population and disrupting the activities of approximately 6.5 million people. This situation necessitated swift and accurate actions for the mitigation and anticipation of forest and land fires. This research aims to classify forest fires in Riau using Machine Learning algorithms, specifically Random Forests. However, a comprehensive understanding of forest fires requires the visualization of Sentinel-2 satellite imagery using the Normalized Burn Ratio (NBR) index. Sentinel-2 imagery recreates a pivotal role in identifying burnt areas, measuring fire intensity, and assessing environmental impacts. Weather data spanning from January 2015 to September 2019, totaling 1733 data points have been utilized in this study. Experimental results demonstrate that the Random Forest algorithm achieved the highest accuracy of 71% with an 90% training data allocation. Meanwhile, Sentinel-2 imagery can visualize burnt areas with an overall accuracy of 94% and a kappa coefficient of 0.92. This study offers an integrated approach to addressing forest fires in Riau, resulting in improved predictions and a deeper understanding of forest fire disasters. In the context of disaster mitigation, the combination of Machine Learning and Sentinel-2 imagery visualization holds significant potential for providing critical information to stakeholders and authorities]]>
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