Crime Clustering Analysis Based on the Education Level Using the K-Means and Silhouette Coefficient Testing Method
DOI:
https://doi.org/10.11594/nstp.2025.4807Keywords:
Crime, clustering, k-means algorithmAbstract
This study aims to analyze crime clustering based on the education level of correctional prisoners in a Ternate Class II Detention Centre using the K-Means method and silhouette coefficient testing. The K-means method is used to group correctional prisoners’ data based on the level of education and the type of crime committed. Silhouette coefficient testing is used to evaluate the quality of the resulting clustering. The clustering results show that correctional prisoners can be grouped into three main clusters based on education level and type of crime. The first cluster is dominated by prisoners with low education levels who tend to commit drug and theft crimes. The second cluster includes correctional prisoners with secondary education who mostly commit crimes of abuse and child protection. The third cluster includes prisoners with higher education who commit various types of crimes, including corruption and decency. This research shows that the K-Means method is effective for clustering WBPs based on education level and crime type, and Silhouette Coefficient testing confirms that the cluster-ing results are of good quality.
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Copyright (c) 2025 Suci Ayu Maharani, Abdul Mubarak, Alfanugrah A H. Usman, Amal Khairan, Rosihan, Salkin Lutfi
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