Grouping of Areas Based on Flood Disaster Level Using K-Means Clustering Algorithm - Maryam Hasan; Sudirman S. Panna; Abd. Rahmat Karim Haba; Apriyanto Alhamad (Jambura Journal of Electrical and Electronics Engineering, 2026)
The Province of Gorontalo is highly vulnerable to flood disasters due to its geographical conditions, high rainfall, and uncontrolled land-use changes. This study aims to apply the K-Means Clustering algorithm to classify regions based on flood impact levels to support disaster mitigation and decision-making processes by the National Search and Rescue Agency (BNPP) Gorontalo. The dataset comprises 405 disaster incident records obtained from related institutions, including the number of affected, injured, deceased, and missing individuals. The analysis process involves data collection, preprocessing, distance calculation using the Euclidean Distance method, and the formation of two clusters based on impact levels. The iteration process stopped at the second iteration, indicating that a stable (convergent) condition had been achieved. The results revealed that Cluster 1 (C1) includes areas significantly affected by floods such as Imana, Iloheluma, and Tudi villages, while Cluster 2 (C2) represents unaffected areas like Wapalo, Ilomata, Motihelumo, and others. The implementation of the K-Means algorithm proved effective in identifying disaster-prone regions objectively and data-driven, thus supporting more efficient disaster response planning.