Implementation of K-Means Clustering on Poverty Indicators in Indonesia

  • Suwardi Annas Universitas Negeri Makassar
  • Bobby Poerwanto Universitas Cokroaminoto Palopo
  • Sapriani Sapriani Universitas Negeri Makassar
  • Muhammad Fahmuddin S Universitas Negeri Makassar
Keywords: Poverty Gap Index, Poverty Severity Index, K-Means, Machine Learning, Cluster Analysis


This study aims to cluster all districts/cities in Indonesia related to poverty indicators. The attributes used are poverty gap index and poverty severity index. The data used comes from BPS. The method used is K-Means clustering, and the results show that by using the elbow and silhouette index methods, the optimal number of clusters is 2, where for cluster 1, it can be defined as a cluster with an area with a high poverty gap index and poverty severity index compared to cluster 2. As a result, cluster 1 has 42 districts/cities, and 472 for cluster 2.


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How to Cite
Annas, S., Poerwanto, B., Sapriani, S., & S, M. (2022). Implementation of K-Means Clustering on Poverty Indicators in Indonesia. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 21(2), 257-266.