<![CDATA[Crime is a complex social problem that affects the security of the community, especially in the city of Semarang. Therefore, in an effort to deal with the increasing crime, the use of information technology and data analysis becomes very relevant. This research aims to implement data mining algorithm K-Means Clustering in analyzing crime patterns in Semarang City. This research method involves the use of historical crime data from January to December 2022 with a total data of 305 incidents in Semarang City. The K-Means Clustering algorithm in data mining was chosen because of its ability to group data based on similar characteristics effectively. The data was analyzed using RapidMiner software, which facilitated the clustering of crime patterns into seven clusters cluster 1 with 60, cluster 2 with 31, cluster 3 with 36, cluster 4 with 35, cluster 5 with 51, cluster 6 with 55, and cluster 7 with 37. These findings provide a strong basis for the police to design more targeted and efficient crime handling strategies. The implementation of the K-Means Clustering algorithm in this study proved effective in identifying crime patterns and providing useful insights for security policy decision-making. This research also opens up opportunities for the development of more sophisticated information systems in city security management in the future]]>
Copyrights © 2024
