Clustering Criminal Offense Areas in the Kolaka Police Jurisdiction Using the K-Means Method

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Diterbitkan: 2026-06-30
Kata Kunci:
Clustering, Crime Mapping, Euclidean Distance, K-Means, Web Information System

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SISKA NUR FAJRIANI
Universitas Sembilanbelas November Kolaka
Rabiah Adawiyah
Universitas Sembilanbelas November Kolaka
Yuwanda Purnamasari Pasrun
Universitas Sembilanbelas November Kolaka

Abstrak

Criminal offenses are a common problem that occurs in daily life, including in Kabupaten Kolaka, Indonesia. Different types of crime occur at different places, times, and frequencies, so that the annual crime rate fluctuates and community members - and the police in particular - find it difficult to determine which areas require closer supervision. This study designs and builds a web-based information system that groups the sub-jurisdictions of Polres Kolaka according to their level of criminality using the K-Means clustering algorithm. The system was developed with the waterfall model, covering requirement analysis, design (data flow diagrams, an entity-relationship diagram, and flowcharts), PHP/MySQL implementation, and black-box testing. A dataset of 550 recorded cases across 8 sub-jurisdictions and 8 offense categories (theft, immorality, armed robbery, assault, fraud, illegal liquor, gambling, and narcotics) was clustered into three groups - low, moderate, and high criminality - using Euclidean distance with randomly selected initial centroids. The clustering process converged after four iterations, when the same cluster membership was obtained in two consecutive iterations. The results show that Polres Kolaka forms its own high-criminality cluster, Polsek Wundulako, Polsek Pomalaa, and Polsek Watubangga form a moderate cluster, while the remaining four sub-jurisdictions form a low-criminality cluster. All seven black-box test scenarios were valid, and the clustering results produced by the system matched the manual computation for all 8 sub-jurisdictions. The system provides the public and, in particular, the police with quick, accurate information on crime-prone areas to support patrol planning and resource allocation.

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Vol 1 No 1 (2026): Advancing Knowledge Through Data, Empowering Decisions

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Clustering Criminal Offense Areas in the Kolaka Police Jurisdiction Using the K-Means Method. (2026). ENSEMBLE : Journal of Applied Mathematics, Statistics and Data Science, 1(1), 1-9. https://statdatajournal.com/ensemble/article/view/8

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