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Crime is a social issue that continues to evolve alongside increasing community activity and regional development. This study aims to Cluster crime data in Binjai City based on the location of incidents using the K-Means algorithm and the Cross Industry Standard Process for Data Mining (CRISP-DM) approach. The data were obtained from the Binjai Police Department, with attributes including the type of crime, time of occurrence, and location, categorized by district. A comprehensive data preprocessing stage was carried out, involving the extraction of information from raw data, normalization of crime type labels, and conversion of categorical data into numerical form using label encoding. The optimal number of Clusters was determined using the Silhouette score method, which yielded the best result at K = 10. The Clustering results were further evaluated using the Davies-Bouldin Index (DBI) to ensure Cluster quality. The analysis revealed that Binjai Utara District has the highest number of crimes, particularly aggravated theft (curat), which frequently occurs from early morning to late morning. This Clustering is expected to provide valuable insights for authorities in formulating more targeted and data-driven regional security strategies.

Crime is a problem experienced by humans from time to time, crime often occurs because of several factors, one of which is due to the lack of security of the address so that many criminal acts occur. Hamparan Perak Police is trying to increase its commitment to safeguard and protect the community through efforts that are organized consistently and continuously. The rise of criminal acts that occur, such as motorcycle theft, persecution, and the rise of robbery in the middle of the road makes residents feel unsafe and always feel threatened at certain addresses. Therefore, to determine the vulnerable pattern of crimes committed, it is necessary to determine the group to determine the vulnerable area or not using the clustering method, which aims to be able to assist the police in conducting socialization and actions for public security by combining objects in a group with each other and different from objects in other groups. From the tests carried out using the clustering method with the K-Means algorithm, it can be seen that the group of criminal data that has the highest group and most often appears when processed is the criminal act of theft, the pattern of criminal acts in quiet areas, has been monitored and planned in klambir village.