The province of Riau frequently faces the threat of forest and land fires, which have recurred every dry season since 1997. Major forest fires in 1997 and 2015 were among the most severe disasters. These fires have had widespread impacts, including environmental pollution, respiratory illnesses, and even fatalities. The resulting haze has also disrupted transportation and economic activities and spread to neighboring countries such as Malaysia and Singapore, straining regional political relations. The geographical conditions of Riau Province, dominated by peatlands, along with extreme weather characterized by low rainfall and high temperatures, increase the risk of forest and land fires. Additionally, poor land management, such as excessive peat canalization and sporadic land clearing, further contributes to the high fire risk. To mitigate the impact of forest and land fires, identifying fire-prone areas is crucial, especially through the analysis of hotspots or heat points. Identifying fire-prone areas can be achieved using a clustering approach. A total of 10,839 hotspot data points from the BRIN Fire Hotspot website will be grouped based on their density using the density-based clustering algorithm Ordering Points To Identify the Clustering Structure (OPTICS). OPTICS is chosen for its ability to process spatial location data with irregular distributions and its higher accuracy compared to similar algorithms like DBSCAN. The data used includes latitude and longitude coordinates of each hotspot. The clustering results are expected to produce a fire risk map that will be useful for relevant agencies in responding to and mitigating disasters quickly and effectively.