Analisis dan Klasifikasi Kabupaten/Kota di Provinsi Aceh, Sumatera Utara, dan Sumatera Barat Berdasarkan Karakteristik Pendidikan, Ketenagakerjaan, Lingkungan, Kesejahteraan, dan Ekonomi Menggunakan Metode K-Means
DOI:
https://doi.org/10.61722/jssr.v4i6.13187Keywords:
K-Means, Clustering, Economic Welfare, Regional Development, Regional HeterogeneityAbstract
Inequality in development characteristics between regions is one of the important issues in regional development planning because differences in the conditions of education, employment, environment, welfare, and economy produce diverse regional characteristics. This condition is increasingly relevant in Aceh Province, North Sumatra, and West Sumatra, which in 2025 face various hydrometeorological disasters, such as floods, flash floods, and landslides, that have the potential to affect social conditions and regional development. This study aims to identify and group regencies/cities based on the similarity of multidimensional characteristics of education, employment, environment, welfare, and economy using the K-Means clustering method with Euclidean distance. The data used are secondary data for 2025 on 75 regencies/cities in the three provinces. The analysis stages include data standardization, determination of the number of clusters, cluster formation using the K-Means algorithm, and interpretation of group characteristics based on centroid values. The analysis results produce five clusters with member proportions of 4.00%, 34.67%, 2.67%, 26.67%, and 32.00%, respectively. The overall Silhouette value of 0.268488 indicates that the cluster structure still has a relatively limited degree of separation, so the clustering results need to be interpreted carefully. Based on the Euclidean distance between cluster centers, the largest distance of 1.474 is between Cluster 1 and Cluster 5, while the smallest distance of 0.323 is between Cluster 2 and Cluster 4. Cluster 1, consisting of Medan, Deli Serdang, and Langkat, is characterized by very high education and employment, while Cluster 5 shows relatively the highest welfare and economic characteristics. These characteristic differences show that development improvement does not occur uniformly across all regions. Therefore, the clustering results can be used as preliminary information to set intervention priorities, identify regions with similar characteristics, and support the formulation of more targeted development policies based on the empirical conditions of the region. These findings confirm the existence of development heterogeneity between regions, so development policies need to be designed based on the specific characteristics and needs of each regional group.
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