<![CDATA[Riau Province is one of the regions in Indonesia that is prone to forest and land fires (karhutla), especially during the dry season. Accurately identifying the distribution patterns of fire-prone areas is crucial in supporting disaster mitigation and management efforts. This study aims to identify the spatial patterns (Cluster) of forest and land fire-prone areas (karhutla) in Riau Province using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm integrated with a Geographic Information System (GIS). This study uses NASA-MODIS data taken from 2020 to 2024 with 840 data records. The analysis results show that DBSCAN is able to effectively group hotspots, with Cluster 2 being the largest cluster covering 297 karhutla points in Bengkalis, Rokan Hilir, and Dumai. The large number of points in this cluster is due to the high frequency of forest and land fires between 2020 and 2024. However, Cluster 7 shows the best density quality with a Silhouette Coefficient value of 0.872, surpassing Cluster 2 which has a value of 0.638. The overall average Silhouette Coefficient value is 0.683, indicating that the cluster modeling is quite optimal. A total of 57 hotspots are categorized as noise, but still provide a picture of the distribution of forest and land fires. GIS-based map visualization reveals that most fire hotspots are located in peatlands and dry vegetation areas that are consistent from year to year. The results of the study confirm that the use of appropriate DBSCAN parameters (epsilon and minPts) produces accurate spatial visualization and supports more effective and targeted mitigation strategies and fire monitoring based on priority areas. ]]>
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