Comparison of Cluster Average Linkage and K-Means Analysis Methods for Poverty Grouping in The Nusa Tenggara Area

Muhammad Alimuddin; Lisa Harsyiah; Zulhan Widya Baskara

doi:10.29303/emj.v9i1.243

Authors

Muhammad Alimuddin Department of Mathematics, University of Mataram
Lisa Harsyiah Department of Statistics, University of Mataram
Zulhan Widya Baskara Department of Statistics, University of Mataram

DOI:

https://doi.org/10.29303/emj.v9i1.243

Keywords:

Average Linkage, Cluster Analysis, K-Means, Poverty

Abstract

Poverty is a problem that often occurs and is a fundamental problem in almost all developing countries, including Indonesia. The Nusa Tenggara region consists of two administrative regions, namely the Provinces of West Nusa Tenggara (NTB) and East Nusa Tenggara (NTT) which have high poverty rates. The increase in the number of poor people was caused by several indicators such as environmental conditions, education, income, health, access to goods and services, and others. The purpose of this research is to determine the best method in the process of classifying poverty with the cluster analysis method. The methods used in this study are the average linkage and K-Means cluster analysis methods, as well as the silhouette index method in terms of cluster validation to obtain the best cluster analysis method. The data used is poverty data for the Nusa Tenggara Region in 2021 which includes four poverty sectors, namely employment, education, health, and housing and the environment. Based on the research results, the best method for grouping is the K-Means cluster analysis method by forming three clusters where the first cluster consists of 3 districts/cities, the second cluster consists of 22 districts/cities, and the third cluster consists of 7 districts/cities. The K-Means cluster analysis method is the best method with the highest silhouette index value of 0.28, higher than the average linkage method which obtained a silhouette index value of 0.27.

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