Clustering Divorce Cases at Kraksaan Religious Court Using K-Means


Authors

(1) * Zainal Arifin   (Universitas Nurul Jadid)  
        Indonesia
(*) Corresponding Author

Abstract


Divorce cases are social and legal issues that require systematic data mapping so that relevant institutions can understand variations in case characteristics across regions. This study aims to cluster divorce cases at the Kraksaan Class 1A Religious Court based on subdistrict, age difference between spouses, and factors causing divorce using the K-Means Clustering algorithm. Primary data were obtained through observation, interviews, and documentation of divorce case records from 2022 to June 2024. The dataset consisted of 2,424 case records, which underwent data selection, data cleaning, categorical-to-numerical transformation, and data aggregation by subdistrict. The variables used included the frequency of age-difference categories and the frequency of divorce-causing factors in each subdistrict. The clustering process used three clusters to map high, medium, and low divorce-case profiles. The results show that Cluster 0 represents the group with the highest divorce-case profile and consists of Maron Subdistrict. Cluster 1 represents the low divorce-case group and consists of nine subdistricts, while Cluster 2 represents the moderate divorce-case group and consists of fourteen subdistricts. Internal evaluation using the Davies-Bouldin Index produced a value of 0.6611. This value indicates reasonably good cluster separation because a smaller DBI value indicates a more compact and better-separated cluster structure. The clustering results were integrated into a Streamlit application to facilitate data display, preprocessing, cluster results, and analytical visualization

Keywords

Clustering; Davies-Bouldin Index; Divorce Cases; K-Means; Streamlit



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