Implementation of K-Means Clustering and Weighted Products in Determining Crime-Prone Locations
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Implementation of K-Means Clustering and Weighted Products in Determining Crime-Prone Locations

Yuni Rahmatika, Eko Sediyono, Catur Edi Widodo


Crime is an act that violates the law. The number of criminal acts that occur becomes a social problem that makes the community and the police uneasy. Increasing the number of crimes is a problem in the social aspect. This research aims to build an information system to provide information on areas prone to a crime that can help the police to speed up the crime resolution process. The grouping process uses the k-means method used to classify based on the level of vulnerability of the area, grouping crime is a good strategy in improving crime prevention planning. In addition to the k-means clustering method, we also use the weighted product method which functions as a recommendation ranking for crime selection. The grouping results obtained from this study are that there is 1 very vulnerable area, 5 areas in the vulnerable category and 3 safe areas. While the weighted product method produces melatilor kudus city 'as a prone to beating areas with a score of 0.182093.


Decision Support System, Data Mining, K-Means Clustering , Weighted Product Method , Crime


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