Pengelompokan Provinsi di Indonesia dengan Metode Hierarchical Density Based Spatial Clustering with Noise (HDBSCAN) (Studi Kasus: Data Perekonomian Provinsi di Indonesia Tahun 2024)
DOI:
https://doi.org/10.17509/Keywords:
HDBSCAN, Analisis Klaster, Klasterisasi, Ketimpangan Ekonomi, Pembangunan DaerahAbstract
Economic inequality between provinces in Indonesia remains a challenge, with the Gini ratio recorded at 0.381 in September 2024. This study clusters the provinces of Indonesia using the HDBSCAN method based on 2024 economic indicators, applying Euclidean and Manhattan distances and evaluating the results with the Silhouette Score (SC) and the Davies-Bouldin Index (DBI). Under the Euclidean metric, the best parameters were min_samples = 2 and min_cluster_size = 3 (SC = 0.4443; DBI = 0.6761; coverage 89.47%), producing two clusters and four noise points. Under the Manhattan metric, the best parameters were min_samples = 1 and min_cluster_size = 2 (SC = 0.5730; DBI = 0.5591; coverage 100%), producing two clusters without noise. Both approaches showed a relatively similar clustering pattern in identifying provinces with lower economic characteristics, while the Euclidean partition provided a finer split by isolating the provinces with dominant rice production. A sensitivity analysis showed that this split disappears when the rice production indicator is removed, whereas the group of provinces with low economic characteristics remains.
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