Penerapan Algoritma K-Means Clustering untuk Segmentasi Kepadatan Penduduk Berbasis GIS - Putri, Rizki Amelia; Safwandi, Safwandi; Fitri, Zahratul (JURIKOM (Jurnal Riset Komputer), 2025)
This study aims to develop a clustering system using the K-means algorithm to analyze demographic data of sub-districts from 2020 to 2023. The system is designed to cluster sub-districts based on variables such as population size, population percentage, population density, and gender ratio. The clustering results reveal different grouping patterns each year, reflecting the dynamics of demographic data over time. Evaluation using the Davies-Bouldin Index (DBI) indicates that the clustering results are of reasonably good quality, with DBI values of 1.1492 in 2020, 0.6859 in 2021, 1.2470 in 2022, and 0.6805 in 2023. The best DBI value was recorded in 2023 at 0.6805, demonstrating that the clustering results in that year were the most optimal compared to other years. The system also facilitates Users with interactive map visualizations, supporting better data analysis and decision-making processes. This research is expected to contribute to the management of demographic data and support more accurate data-driven policy-making.