Implementation of HDBSCAN and Bayesian Optimization for Clustering Flood-Affected Regions in Indonesia
DOI:
https://doi.org/10.30871/jaic.v10i3.12734Keywords:
Bayesian Optimization, Clustering, DBCV, Floods, HDBSCANAbstract
Floods are among the most frequent natural disasters in Indonesia, with thousands of events causing significant impacts on infrastructure damage and human lives. The substantial increase in the number of victims and flood-related damages in 2024 indicates that flood disaster mitigation efforts in Indonesia remain suboptimal. Consequently, a clustering-based analytical approach is required to understand patterns of flood impact across provinces. This study aims to cluster provinces in Indonesia based on flood-affected indicators using the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) method with Bayesian Optimization to obtain optimal hyperparameters. This study comprises several stages, including data collection, data standardization, statistical test, data reduction, hyperparameter optimization, HDBSCAN algorithm, model evaluation, and analysis of clustering results. The results show that HDBSCAN with Bayesian Optimization yields a well-separated cluster structure with a DBCV value of 0.515. The clustering results consist of three primary clusters and one noise cluster. Cluster 0 (High Displacement & Inundation) consisting of 5 provinces, cluster 1 (High Fatality & Structural Damage) consisting of 4 provinces, cluster 2 (Low Impact) consisting of 21 provinces, and the noise cluster consisting of 8 provinces. These findings are intended to provide a foundation for the government to formulate targeted flood mitigation strategies tailored to the flood impact characteristics of each province.
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