ST-DBSCAN Algorithm Implementation At Riau Province Forest Fire Points (2015-2022) - Kemal El Faraouk; Harry Witriyono; Dwita Deslianti; Nuri David Maria Veronika (Penerbit Jurnal Indonesia, 2023)
The forest conditions in Indonesia require more serious attention as they are constantly disturbed, including incidents of forest fires. Clustering or grouping using the ST-DBSCAN algorithm will group forest fire points based on distance and time. This data can be obtained from the FIRMS (Fire Information for Resource Management System) website, which utilizes MODIS sensor data. The research implements the ST-DBSCAN algorithm using the R language, focusing on a case study in the Riau Province from 2015 to 2022. The parameters used in this research for the ST-DBSCAN algorithm are Eps1 = 0.7, Eps2 = 2, and MinPts = 2. The algorithm generates several types of clustering patterns, including Stationary, Reappearing Regular, Irregular, Occasional, and Tracks. The fire point data used in this research covers the years 2015, 2016, 2017, 2018, 2019, 2020, 2021, and 2022 in the Riau Province. The results obtained from this research include 1 Reappearing Regular pattern, 5 Tracks patterns, 1 Reappearing Irregular pattern, and 1 Stationary pattern. Within the time frame of 2015, 2016, 2017, 2018, 2019, 2020, 2021, and 2022, the highest occurrence of forest fire spots happened in the month of November 2015, reaching a total of 573 fire spots.