<![CDATA[This study extends previous research that clustered the 2019 Human Development Index (HDI) data of regencies and cities in Indonesia using K-Means, K-Medoids, and Agglomerative Hierarchical Clustering (AHC). HDI is an important indicator for describing the level of regional development; therefore, clustering analysis of HDI data is needed to support more targeted development policy formulation. However, these conventional clustering methods have limitations, including the requirement to predefine the number of clusters and their limited ability to handle noise. Therefore, this study applies and compares two density-based clustering algorithms, namely DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and Mean Shift, which are capable of forming clusters automatically without specifying the number of clusters in advance and can effectively handle noise. The determination of optimal parameters for each method is conducted using the Sw/Sb Ratio metric, which measures the ratio between within-cluster and between-cluster standard deviations. The results show that Mean Shift with an optimal bandwidth parameter of 1 achieves an Sw/Sb Ratio value of 0.3609, which is better than DBSCAN with a value of 0.3739, and also outperforms the clustering methods used in previous studies, which produced a value of 0.51. These findings indicate that density-based clustering algorithms, particularly Mean Shift, provide more representative clustering results for HDI data and may serve as a more effective alternative method for analyzing human development data in Indonesia.]]>
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