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


Clustering algorithms can be used to build geographic mapping systems to determine crime-prone locations. This study aims to establish a geographical mapping system to determine crime-prone locations that can help police control certain locations that often occur crime and provide information to people in crime-prone locations. Criminal groups are calculated based on crime data from November 2018 to October 2019 which occurred in 9 districts in Kudus Regency. The crime grouping process uses the k-means method used to classify based on regional vulnerability and uses a weighted product method that functions as a vulnerability ranking that is vulnerable to crime selection. The grouping results obtained from this study are that there are 1 very vulnerable area, 5 areas in the vulnerable category, and 3 safe areas. While the weighted product method produces Melatilor area as a vulnerable area to be defeated by a score of 0.182093. This research provides benefits for the public to see crime-prone areas so that they can be more vigilant, while for the police to analyze crime so as to speed up the process of resolving crime and increase and improve crime prevention measures.


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

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