Analisis Perbandingan Algoritme K-Means dan Isodata untuk Klasterisasi Data Kejadian Titik Api di Wilayah Sumatera pada Tahun 2001 hingga 2014 - Edo Fadila Sirat; Budi Darma Setiawan; Fatwa Ramdani (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2018)
Fire phenomenon is a familiar phenomenon in Indonesia. The high number of fire incidents in Indonesia requires special attention from the government, so that any natural disasters such as forest fires can be overcomed. Satellite monitoring results are recorded on a data file of fire points with a large enough data numbers so that the data is difficult to be processed to become information that is easily received by the user. Based on data obtained from the EOSDIS site recorded as many as 289,256 fire spots occurrence in the region of Sumatra in the timeframe between 2001 to 2014. It takes an algorithm to segment the data or cluster the data, so that large data can be processed into a good information for the user. In this study a comparative study of clustering algorithms between K-Means and Isodata was conducted. Both algorithms used in this study were assessed based on the quality of the clusters produced. The algorithm used in measuring the quality of cluster in this research is Silhouette Coefficient (SC). The final result value of Shilhouette Coefficient K-Means method is 0.999997187 and Isodata method is 0.999957161, so in this case, K-Means algorithm has a higher SC value compared to the Isodata algorithm in clustering the data of fire spots with a small SC value difference.