Analisis Perbandingan Pengelompokan Indeks Pembangunan Manusia Indonesia Tahun 2019 dengan Metode Partitioning dan Hierarchical Clustering

  • Arina Mana Sikana Politeknik Statistika STIS
  • Arie Wahyu Wijayanto Politeknik Statistika STIS

Abstract

Human Development Index (HDI) is an important indicator in measuring the level of success of the development of the quality of human life. Human Development Index clustering aims to divide the regions into groups based on Human Development Index for the region in 2019. Human Development Index clustering compares Partitioning Clustering and Hierarchical Clustering method to divide Human Development Index Indonesia in 2019. Partitioning Clustering method uses K-Means Clustering algorithm and Hierarchical Clustering method uses Agglomerative Ward Clustering algorithm. The results obtained are the best method for grouping provinces in Indonesia based on Human Development Index in 2019 is K-Means Clustering method with the optimum number of clusters is 6. This method gives Silhoutte Score o0,6291, Calinski-Harabasz Index 241,8875, dan Davies-Bouldin Index 0,3038. While the best method for grouping regencies in Indonesia based on Human Development Index in 2019 is K-Means Clustering method with the optimum number of clusters is 6. This method gives Silhoutte Score 0,5511, Calinski-Harabasz Index 1525,4007, dan Davies-Bouldin Index 0,5234.

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Published
2021-09-30
How to Cite
SIKANA, Arina Mana; WIJAYANTO, Arie Wahyu. Analisis Perbandingan Pengelompokan Indeks Pembangunan Manusia Indonesia Tahun 2019 dengan Metode Partitioning dan Hierarchical Clustering. Jurnal Ilmu Komputer, [S.l.], v. 14, n. 2, p. 66-78, sep. 2021. ISSN 2622-321X. Available at: <https://ojs.unud.ac.id/index.php/jik/article/view/69019>. Date accessed: 27 oct. 2021. doi: https://doi.org/10.24843/JIK.2021.v14.i02.p01.