ANALISIS CLUSTERING PENDUDUK BERDASARKAN KELOMPOK UMUR DENGAN K-MEANS DAN HIERARCHICAL CLUSTERING UNTUK PERENCANAAN DEMOGRAFI
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Abstract
Demographic planning plays a vital role in regional development, helping governments design policies based on the specific needs of each age group. However, demographic data is often complex and challenging to interpret using traditional methods. This study employs K-Means and Hierarchical Clustering to group population data based on age groups. K-Means is utilized for its efficiency, while Hierarchical Clustering provides in-depth insights through dendrogram analysis. The analysis reveals three primary clusters: Cluster 2 (young individuals under 40 years) dominates the population (65%) and is prioritized for investments in education and employment opportunities. Cluster 1 (ages 40–59 years) requires policies aimed at enhancing work productivity, while Cluster 0 (elderly individuals over 60 years) necessitates elderly healthcare services and social welfare programs. The selection of three clusters as the optimal solution is supported by evaluation metrics such as the Silhouette Score and the Calinski-Harabasz Score. These results demonstrate that clustering methods are effective in analyzing demographic data, offering profound insights to support sustainable, data-driven planning. Furthermore, this research contributes to the academic literature on demographic data analysis using clustering techniques.
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