<![CDATA[The objective of this research is to identify the most effective clustering method for grouping Indonesian provinces by labor–economic indicators to support more precise, data-driven policy formulation. Regional disparities in Indonesia’s economic growth, driven by unequal labor characteristics, remain a significant obstacle to achieving inclusive development. An analytical approach capable of grouping provinces by labor and economic indicators is therefore essential. This study applies a comparative clustering analysis using three unsupervised algorithms: Self-Organizing Maps (SOM), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and K-Affinity Propagation (K-AP). The dataset consists of five key indicators, namely economic growth, total population, labor force, employment rate, and average wage level obtained from Statistics Indonesia (BPS) for the year 2024. The clustering performance is evaluated using internal validation criteria based on the ratio of within-cluster variation (Sw) to between-cluster variation (Sb), where a smaller ratio indicates more compact, well-separated clusters. The results show that each method produces different clustering structures. SOM and DBSCAN generate three clusters with varying provincial distributions, whereas K-AP produces five clusters with more balanced, representative groupings. The evaluation results indicate ratios of 3.1906 for SOM, 0.2000 for DBSCAN, and 0.1779 for K-AP, indicating that K-AP provides the most optimal clustering performance. These findings confirm that K-Affinity Propagation is the most effective and stable method for classifying Indonesian provinces by labor and economic characteristics. The outcomes of this study provide empirical insights and analytical references for labor-driven economic policy formulation and data-driven regional development planning in Indonesia.]]>
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