Penggunaan Hierarchical Agglomerative Clustering Dalam Pengelompokan Kabupaten/Kota Berdasarkan Tingkat Kesejahteraan Di Sulawesi Selatan
DOI:
https://doi.org/10.30605/proximal.v9i1.8055Keywords:
Agglomerative Clustering, Average linkage, Principal Component Analysis (PCA), Euclidean, WaefareAbstract
This study aims to classify regencies/municipalities in South Sulawesi Province based on welfare indicators using cluster analysis. Cluster analysis is a technique for grouping objects according to the similarity of their characteristics. The method employed is hierarchical agglomerative clustering with the average linkage approach, which is a stepwise clustering procedure that merges two objects with the shortest distance until a single cluster is formed. The average linkage method defines the distance between clusters as the average distance between all objects in one cluster and all objects in another cluster. Prior to clustering, a sampling adequacy test, correlation test, and Principal Component Analysis (PCA) were conducted to address multicollinearity. Subsequently, the Euclidean distance matrix was calculated, a dendrogram was constructed, and the optimal number of clusters was determined by cutting the dendrogram at the largest difference between successive fusion distances, which indicates a significant change in the cluster structure. The results show that two clusters were obtained. Cluster 1, representing regions with lower welfare levels, consists of Selayar, Bulukumba, Bantaeng, Jeneponto, Takalar, Gowa, Sinjai, Maros, Pangkep, Barru, Bone, Soppeng, Wajo, Sidrap, Pinrang, Enrekang, Luwu, Tana Toraja, Luwu Utara, Luwu Timur, Toraja Utara, Parepare, and Palopo. Cluster 2, representing regions with higher welfare levels, consists of Makassar.
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