Abstract
This study seeks to examine poverty across the regencies and cities on Sumatra Island in 2023 by employing the K-Means Clustering approach. Poverty represents a complicated and multi-faceted societal challenge, shaped by various elements including educational attainment, joblessness, income per capita, and spending per capita. The information utilized in this analysis is sourced from the Central Bureau of Statistics, specifically the proportions of impoverished individuals, average duration of education, rates of open unemployment, income per capita, and expenditure per capita. Findings reveal the establishment of three distinct clusters based on poverty attributes: Cluster 1 exhibiting a low poverty level, Cluster 2 displaying a moderate poverty level, and Cluster 3 indicating a high poverty level. Results from the One Way Anova test indicate notable differences in poverty traits across the clusters. It is anticipated that this research will aid the government in developing more suitable and effective strategies for tackling poverty in regions grappling with significant poverty challenges.
