Comparison of K-Means and Hierarchical Clustering Methods in Mapping Socioeconomic Conditions of Regencies and Municipalities in Papua Provinces
DOI:
https://doi.org/10.32493/sm.v8i2.60424Keywords:
hierarchical clustering, K-Means Cluster, Cluster Analysis, Socio-economic indicatorsAbstract
This study aims to classify regencies and municipalities in Papua Province based on their socioeconomic conditions using the K-Means and Hierarchical Clustering methods, as well as to determine the clustering method that provides the best performance. The data used in this study consist of socioeconomic indicators of regencies and municipalities in Papua Province obtained from publications of Statistics Indonesia (BPS). The analysis procedures include data collection, data standardization, implementation of K-Means and Hierarchical Clustering, and evaluation of clustering results using the Silhouette Score. The results indicate that both methods are capable of grouping regions according to their socioeconomic characteristics. However, Hierarchical Clustering achieved a higher Silhouette Score of 0.418 compared to K-Means, which obtained a score of 0.331. This finding suggests that Hierarchical Clustering produces more compact clusters with clearer separation between groups, resulting in better clustering quality. The clustering results are expected to provide valuable insights for local governments in formulating more targeted development policies based on the socioeconomic characteristics of each region in Papua Province.
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