Cluster Analysis of District/City Welfare in Aceh Province: An Application of the K-Means Method - Alifah Alyana; Hasya Zayyan Haziqah; Rini Safariani; Riska Mulyani; Wanda Surianto (Theta: Journal of Statistics, 2025)
Community well-being is a critical determinant of regional development success, particularly in Aceh Province, which exhibits unique social and economic characteristics. This study aims to classify districts and municipalities in Aceh Province based on welfare indicators using the K-Means Clustering method. The analysis employed secondary data from the Central Bureau of Statistics (BPS) for 2023, covering 23 districts/municipalities and 10 indicators: population density, labor force size, labor force participation rate, open unemployment rate, average years of schooling, life expectancy, per capita expenditure, poverty rate, GRDP distribution, and gender development index. The findings reveal that the optimal number of clusters is two. The first cluster comprises regions with relatively lower welfare indicators, while the second cluster consists of regions with higher levels of socio-economic development but facing internal disparities. The silhouette coefficient of 0.309 and the Davies-Bouldin Index (DBI) of 1.065 indicate that the model is reasonably effective in capturing welfare differences across regions. These results provide valuable insights for formulating more targeted and efficient regional development policies