<![CDATA[Socioeconomic conditions across regencies and cities in Indonesia are highly heterogeneous, making it difficult to identify regions with similar welfare levels using conventional clustering methods. Density-based clustering, particularly Ordering Points to Identify the Clustering Structure (OPTICS), can accommodate varying cluster densities and the presence of outliers; however, its performance is influenced by parameter selection. Although optimization approaches have been used to address parameter selection, previous studies have not specifically applied a hybrid Bayesian Optimization and Genetic Algorithm (BO-GA) approach to optimize OPTICS parameters for regional clustering based on socioeconomic indicators. This study aims to optimize OPTICS parameters and identify socioeconomic clusters of regencies and cities in Indonesia in 2024. The data comprise seven socioeconomic indicators for 514 regencies and cities obtained from Statistics Indonesia (BPS). Optimization was performed using one-way hybridization, in which Bayesian Optimization (BO) was initially used to explore the parameter space of MinPts, ε, and xi and generate the top-k solutions as the initial population for the Genetic Algorithm (GA). Subsequently, GA refined the candidate parameters through an evolutionary mechanism, using the Silhouette Coefficient (SC) as the objective function. The results show that the SC increased from 0.612 during the BO stage to 0.687 after the GA stage, with optimal parameters of MinPts = 13, ε = 20.861, and xi = 0.023. Applying these parameters produced two clusters containing 19 and 15 regencies or cities, respectively, while the remaining 480 regions were classified as noise because they were not assigned to either cluster. The indicator profiles suggest that Cluster 0 exhibits relatively lower socioeconomic welfare characteristics than Cluster 1. An SC value of 0.687 indicates relatively good cluster cohesion and separation. Thus, the BO-GA combination was effective in optimizing OPTICS parameters, although most regions remained unclustered. Future research is recommended to incorporate additional socioeconomic and spatial indicators to provide a more comprehensive representation of regional characteristics.]]>
