Forest Fire Clustering in Indonesia Using the Clustering Large Applications (CLARA) Method - Muhammad Arib Alwansyah Arib; Ridya Destriani Ridya; Sigit Nugroho Sigit; Nurul Hidayati Nurul (Universitas Negeri Jakarta, 2025)
Clustering is a process of grouping, observing or grouping classes that have similar objects. One clustering method that handles large amounts of data is clustering large applications (CLARA). This research aims to identify groups of forest fires in Indonesia using the CLARA method and to determine the characteristics of forest fires and the locations of forest fire occurrence points in Indonesia. The data used is hot spot data totaling 3,265 events, which can be obtained from the NASA LANCE–FIRM MODIS Active Fire website. The variables used to group forest fire events are latitude, longitude, brightness, frp and confidence. So by grouping 3,265 hot spot data by determining the optimum cluster using the Shilhoutte index and Dunn index values, the optimum cluster results were obtained, namely 2 clusters