Comparison of Agglomerative Hierarchical Clustering Methods for Grouping Indonesian Provinces Based on Community Literacy Development Index

Authors

  • Olga Afrilly Putri Universitas Negeri Padang
  • Bunga Nafandra Universitas Negeri Padang
  • Zamahsary Martha Universitas Negeri Padang

DOI:

https://doi.org/10.24036/ujsds/vol4-iss1/470

Keywords:

Agglomerative Hierarchical Method, Clustering Analysis, Community Literacy Development Index

Abstract

Community literacy development is one of the important indicators in improving the quality of human resources in Indonesia. This study aims to group provinces in Indonesia based on the Community Literacy Development Index by considering the equity of library services, the adequacy of library collections, and the level of community visits per day. The method used is agglomerative hierarchical cluster analysis. Before grouping, the data is standardized to overcome differences in units and scales between variables. The selection of the best cluster method is done using the cophenetic correlation coefficient, while the determination of the optimal number of clusters uses the silhouette method. The results of the analysis show that the Average Linkage method is the most optimal hierarchical cluster method with the best number of clusters being four clusters. Each cluster has different characteristics, reflecting variations in community literacy levels, service equity, collection adequacy, and library visit intensity between provinces. These findings indicate disparities in community literacy development between regions in Indonesia. Therefore, the results of this study are expected to serve as a basis for consideration in formulating more effective and targeted literacy and library development policies.

Published

2026-03-16

How to Cite

Afrilly Putri, O., Bunga Nafandra, & Zamahsary Martha. (2026). Comparison of Agglomerative Hierarchical Clustering Methods for Grouping Indonesian Provinces Based on Community Literacy Development Index. UNP Journal of Statistics and Data Science, 4(1), 131–140. https://doi.org/10.24036/ujsds/vol4-iss1/470

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