<![CDATA[Population administration is essential for public service delivery and development planning; however, disparities in population document ownership across villages remain a challenge in Bone Bolango Regency. The heterogeneous nature of the data, the presence of outliers, and variations in density patterns limit the effectiveness of classical statistical approaches in capturing the underlying distribution. Therefore, this study aims to compare two density-based clustering algorithms, Ordering Points to Identify the Clustering Structure (OPTICS) and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), in grouping villages based on population document ownership levels. The data were obtained from the Department of Population and Civil Registration of Bone Bolango Regency in 2024 and consist of ownership records of birth certificates, identity cards, and family cards from 165 villages. Both algorithms successfully formed two main clusters representing villages with relatively high and low levels of population document ownership. Internal validation results indicate that OPTICS outperformed HDBSCAN, achieving a Silhouette Coefficient of 0.827, a Davies–Bouldin Index of 0.242, and a Calinski–Harabasz Index of 1217.425, compared to 0.787, 1.210, and 767.866, respectively, for HDBSCAN. In conclusion, OPTICS demonstrates superior capability in producing a more coherent clustering structure for population document ownership data. Therefore, the clustering results obtained using OPTICS can serve as a supporting basis for formulating policies to promote equitable population administration services.]]>
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