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Comparison K-Means and Fuzzy C-Means Methods to Grouping Human Development Index Indicators in Indonesia

Authors

  • Belia Mailien Universtitas Negeri Padang
  • Admi Salma
  • Syafriandi
  • Dina Fitria

DOI:

https://doi.org/10.24036/ujsds/vol1-iss1/4

Keywords:

K-Means, Fuzzy C-Mean, HDI indicator, Cluster Validity

Abstract

The Human Development Index (HDI) is an important indicator to measure the success of efforts to improve people's quality of life. The increase in the human development index in Indonesia is not accompanied by an even distribution of the human development index in every district/city in Indonesia. To facilitate the government in making policies and plans in overcoming the uneven HDI in Indonesia, it is necessary to group districts/cities in Indonesia based on HDI indicators. This study discusses the use of the K-means and Fuzzy C-Means algorithms with a total of 4 clusters. The grouping results obtained summarize that most districts/cities in Papua Island have low HDI indicators. The achievement of the HDI indicator in the medium category on the K-Means and Fuzzy C-Means methods is the same, spread across all major islands in Indonesia. However, the Nusa Tenggara Islands generally have a medium HDI indicator achievement. The achievements of the HDI indicators with high categories in the K-Means and Fuzzy C-Means methods are mostly found on the islands of Sumatra, Java, Kalimantan, and Sulawesi. The achievement of the HDI indicator in the very high category in the K-Means and Fuzzy C-Means methods is found in provincial capitals in several provinces in Indonesia as well as in big cities in Indonesia. The results of this study indicate that the S_DBW index and C_index values of the Fuzzy c-means method are smaller than the K-Means method, namely 2.312 and 0.105.

Published

2022-01-12 — Updated on 2023-02-09

Versions

How to Cite

Belia Mailien, Salma, A., Syafriandi, & Fitria, D. (2023). Comparison K-Means and Fuzzy C-Means Methods to Grouping Human Development Index Indicators in Indonesia. UNP Journal of Statistics and Data Science, 1(1), 23–30. https://doi.org/10.24036/ujsds/vol1-iss1/4 (Original work published January 12, 2022)

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