Identifying Regional Patterns of Poverty in Indonesia: a Clustering Approach Using K-Means

Sri Wahyuni; Agustia Hananto; Baenil Huda; Fitria Apriani; Tukino Tukino

doi:10.29040/ijcis.v6i1.218

Identifying Regional Patterns of Poverty in Indonesia: a Clustering Approach Using K-Means

Sri Wahyuni, Agustia Hananto, Baenil Huda, Fitria Apriani, Tukino Tukino

Abstract

Poverty in Indonesia remains a major challenge, with significant levels of inequality between provinces. This study applies the K-Means clustering method to analyze poverty distribution patterns in 38 provinces in Indonesia, using data on the percentage of poor people from 2010 to 2024. With this approach, provinces are grouped into three main clusters: low, medium, and high, based on the average poverty rate. The low cluster includes areas with poverty rates below 10%, while the medium and high clusters indicate poverty levels that require more specific policies. The evaluation results show a silhouette score of 0.613, indicating that this grouping is quite good but can still be improved. This study offers important insights to support more targeted and effective policies, especially in achieving Sustainable Development Goal (SDG) 1: Eradicating Poverty.

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DOI: https://doi.org/10.29040/ijcis.v6i1.218