BibTex Citation Data :
@article{J.Gauss40229, author = {Leviana Sari and Arief Hakim and agus rusgiyono}, title = {PENGGUNAAN INDEX CALINSKI-HARABASZ PADA CLUSTERING K–MEDOIDS ALGORITHM UNTUK PENGGOLONGAN KABUPATEN/ KOTA DI PROVINSI JAWA TENGAH BERDASARKAN KARAKTERISTIK PENDUDUK MISKIN}, journal = {Jurnal Gaussian}, volume = {14}, number = {1}, year = {2025}, keywords = {Poverty ,Clustering, K-Medoids Clustering, Index Calinski-Harabasz.}, abstract = { Poverty is a problem that occurs almost every year. The government is trying hard to reduce poverty because the poverty rate is a measure of the success of a region. Clustering analysis can assist the government in providing targeted assistance. The k-medoids method is a non-hierarchical clustering method for classifying n objects into k clusters based on similar characteristics. This clustering algorithm uses the medoid as its cluster center. The k-medoids method used to overcome the problem of outliers and determine the optimal number of clusters using cluster validation. This research used Index Calinksi-Harabasz. Based on the result of the clustering, the optimal cluster was obtained k=2 using k-medoids method and cluster validation Index Calinksi-Harabasz of 31,53654 . Cluster 1 consists of 22 districts or cities and in cluster 2 consists of 13 districts or cities. }, issn = {2339-2541}, pages = {179--187} doi = {10.14710/j.gauss.14.1.179-187}, url = {https://ejournal3.undip.ac.id/index.php/gaussian/article/view/40229} }
Refworks Citation Data :
Poverty is a problem that occurs almost every year. The government is trying hard to reduce poverty because the poverty rate is a measure of the success of a region. Clustering analysis can assist the government in providing targeted assistance. The k-medoids method is a non-hierarchical clustering method for classifying n objects into k clusters based on similar characteristics. This clustering algorithm uses the medoid as its cluster center. The k-medoids method used to overcome the problem of outliers and determine the optimal number of clusters using cluster validation. This research used Index Calinksi-Harabasz. Based on the result of the clustering, the optimal cluster was obtained k=2 using k-medoids method and cluster validation Index Calinksi-Harabasz of 31,53654. Cluster 1 consists of 22 districts or cities and in cluster 2 consists of 13 districts or cities.
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