Analisis Klasterisasi Wilayah Berdasarkan Tingkat Kepadatan Penduduk Menggunakan Algoritma K-Means Berbasis Sistem Informasi Geografis
DOI:
https://doi.org/10.47065/bulletincsr.v6i2.1015Keywords:
Population Density; Geographic Information System; K-Means; Clustering; Spatial MappingAbstract
This study aims to analyze and map the population density of regencies and municipalities in Jawa Tengah using a spatial analysis approach based on Geographic Information Systems (GIS) and the K-Means clustering algorithm. The main issue addressed is the lack of systematically classified and informative population density mapping to support spatial analysis and regional decision-making. Secondary data were obtained from the Central Bureau of Statistics (BPS), including total population, population growth rate, population percentage, population density per square kilometer, and administrative boundary spatial data. Prior to clustering, all variables were normalized using the Min-Max scaling method to prevent bias caused by differences in variable ranges in Euclidean distance calculations. The research employed a quantitative descriptive method with K-Means (K=3) to classify regions into low, medium, and high population density clusters. The results indicate that out of 35 regencies/municipalities, 7 regions (20%) fall into the high-density cluster, 22 regions (62.86%) into the medium-density cluster, and 6 regions (17.14%) into the low-density cluster. The implementation of the clustering results into a thematic map using a color scheme of red (high), yellow (medium), and green (low) effectively visualizes spatial distribution patterns, thereby supporting data-driven regional planning and spatial-based policy formulation.
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