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Submitted
Dec 21, 2023
Accepted
Dec 30, 2023
Published
Dec 31, 2023
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Abstract

The Human Development Index (HDI), which takes into account three fundamental aspects of human existence, a long and healthy life, knowledge, and a reasonable level of living, is one tool used to assess the effectiveness of human progress. Clustering provinces based on the human development index is important so that development disparities can be identified and help identify provinces with high, medium or low levels of development. The purpose of this study was to use the k-medoids approach to perform a cluster analysis of HDI in Indonesia based on life expectancy, average years of schooling, expected years of schooling, and expenditure per capita adjusted for 2022. The analysis indicate that two clusters were created: cluster 1 had a high human development index, while cluster 2 had a low human development index. More provinces belonged to cluster 1 than cluster 2 suggesting that human development index in Indonesia in 2022 was largely in the high category

Keywords

Clustering Human Development Human Development Index K-Medoids

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How to Cite
Erda, G., Usdika, R. K., Pitri, R., & Erda, Z. (2023). IMPLEMENTATION OF THE K-MEDOIDS METHOD IN CLUSTERING HUMAN DEVELOPMENT INDEXES IN INDONESIA. Parameter: Journal of Statistics, 3(2), 61-67. https://doi.org/10.22487/27765660.2023.v3.i2.16906

References

  1. Badan Pusat Statistik. (2022). Indeks Pembangunan Manusia. Badan Pusat Statistik, https://news.ge/anakliis-porti-aris-qveynis-momava.
  2. Erda, G., Mega Aulia, S., & Erda, Z. (2023). Classifiying The Factors Influencing The Human Development Index in Riau Province using Principal Component Analysis. Parameter: Journal of Statistics, 2(3), 17–23. https://doi.org/10.22487/27765660.2022.v2.i3.16203
  3. Gao, S., Meng, F., Gu, Z., Liu, Z., & Farrukh, M. (2021). Mapping and Clustering Analysis on Environmental, Social and Governance Field a Bibliometric Analysis Using Scopus. Sustainability, 13(13), 7304. https://doi.org/10.3390/su13137304
  4. Gore, P. A. (2000). Cluster Analysis. In Handbook of Applied Multivariate Statistics and Mathematical Modeling (pp. 297–321). Elsevier. https://doi.org/10.1016/B978-012691360-6/50012-4
  5. Hartama, D., & Anjelita, M. (2022). Analysis of Silhouette Coefficient Evaluation with Euclidean Distance in the Clustering Method (Case Study: Number of Public Schools in Indonesia). Journal Mantik, 6(3), 667–3677.
  6. Mustajab, R., Aristawidya, R., Puspita, L., & Widodo, E. (2021). Aplikasi Metode K-Medoid pada Pengelompokan Kabupaten/Kota di Jawa Barat berdasarkan Indikator Indeks Pembangunan Manusia Tahun 2020. Jurnal Statistika Dan Aplikasinya, 5(2), 221–229.
  7. Paramartha, G. N. W., Ratnawati, D. E., & Widodo, A. W. (2017). Analisis Perbandingan Metode K-Means Dengan Improved Semi-Supervised Analisis Perbandingan Metode K-Means Dengan Improved Semi- Supervised K-Means Pada Data Indeks Pembangunan Manusia (IPM). Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, Vol. 1(9), 813–824.
  8. Sindi, S., Ningse, W. R. O., Sihombing, I. A., R.H.Zer, F. I., & Hartama, D. (2020). Analisis Algoritma K-Medoids Clustering Dalam Pengelompokan Penyebaran Covid-19 Di Indonesia. Jurnal Teknologi Informasi, 4(1), 166–173. https://doi.org/10.36294/jurti.v4i1.1296
  9. Wicaksono, A. S., & Yolanda, A. M. (2021). Pengelompokkan Kabupaten / Kota di Provinsi Nusa Tenggara Timur Berdasarkan Indikator Indeks Pembangunan Manusia Menggunakan K-Medoids Clustering Penyedia Data Statistik Berkualitas untuk Indonesia Maju Pengelompokkan Kabupaten / Kota di Provinsi Nusa Ten. Statistika Terapan, 1(1), 79–90.