Clustering pada Sistem Informasi Pengaduan Masyarakat Fitur Geolokasi untuk Penanganan Kasus Kriminal - Syarifudin, Zaini; Rakhmat Kurniawan R; Triase, Triase (LPPM Universitas Mulia, 2025)
The increasing crime rate in Medan City required structured handling and analysis efforts to prevent and reduce crime. This study aimed to group areas in Medan City based on crime rates using the K-Means Clustering algorithm. The data used were obtained from the Medan Police in 2021, covering various types of crimes in 14 sub-districts. The method applied was K-Means Clustering with 3 clusters to group areas based on crime rates, with the geolocation feature in the analysis system enhancing the accuracy of grouping and identifying crime patterns. The clustering process was carried out through two iterations, where the second iteration showed a more optimal ratio (0.0894) compared to the first iteration (0.0799). The analysis results showed the formation of three groups of areas, namely: the first cluster (high crime rate) included the Criminal Investigation area, the second cluster (moderate crime rate) covered 8 sub-districts, and the third cluster (low crime rate) consisted of 5 sub-districts. The results of this grouping could be used by Polrestabes Medan as a reference for allocating security resources and developing crime prevention strategies tailored to the characteristics of each region. This study proved that the K-Means algorithm was effective in analyzing and grouping regions based on crime rates.