Clustering Crime-Prone Areas in Indonesia Using the K-Means Method - Chairani, T.Sofia; Listia, Hijka; Wardaniah, Sabina; Wulandari, Siti; Tampubolon, Putri Tasya Agustina; Piliang, Arnita (Bit (Fakultas Teknologi Informasi Universitas Budi Luhur), 2024)
Crime in Indonesia is a significant problem that affects various aspects such as security, social, and economic. However, mitigation efforts are often hampered by a lack of structured information about crime-prone areas. This study aims to overcome this problem by grouping provinces in Indonesia based on crime patterns using the K-Means Clustering algorithm. Crime statistics data for 2014-2023 from the Central Statistics Agency (BPS) were analyzed by determining the optimal number of clusters that resulted in five clusters with evaluation using Python and the Scikit-learn library. The results showed a Silhouette Score of 0.593, which reflects the formation of a fairly good cluster. This clustering provides data-driven guidance for the government in developing more targeted security policies to reduce crime rates in Indonesia.