Perbandingan Metode Euclidean dan Manhattan Distance dalam Implementasi Algoritma K-Means dan K-Medoid pada Pengelompokkan Faktor Dominan Perceraian di Kabupaten Bojonegoro - Elok Salma Nabila Salma; Ifnu Wisma Dwi Prastya; Ita Aristia Sa’ida (JURIKOM (Jurnal Riset Komputer), 2026)
The divorce rate in Bojonegoro Regency continues to increase, driven by various social factors such as constant disputes, economic pressure, and household disharmony. Consequently, an analysis is required to map dominant and non-dominant factors more effectively. This study aims to group the factors causing divorce in Bojonegoro Regency for the 2021–2023 period and determine the most optimal clustering method. The research utilizes K-Means and K-Medoids algorithms with Euclidean and Manhattan distance metrics applied to both raw data and data normalized using the Min–Max Scaler, evaluated via the Silhouette Score. The results indicate that data normalization improves cluster quality, and K-Means with Manhattan distance on normalized data achieves the best performance, yielding a Silhouette Score of 0.849547. Cluster displacement analysis reveals that the grouping patterns remain relatively consistent across years, with "constant disputes" consistently emerging as the dominant factor, while other factors remain in the non-dominant cluster with similar patterns. This study demonstrates that K-Means with Manhattan distance on normalized data is more effective for clustering divorce factors. These findings can serve as a methodological foundation for the local government in formulating data-driven social policies and interventions.