Analisis Clustering Pada Data Informasi Penduduk Miskin Di Indonesia Tahun 2021

Poverty is a condition characterized by the inability to meet basic needs such as food, clothing, shelter, education, and healthcare. Different regions in Indonesia naturally have varying numbers of poor populations, as well as supporting indicators. This issue poses challenges for the government in addressing poverty among individuals with different sets of indicators. Grouping the poor population along with their characteristics can greatly assist the government in tackling this issue. This research will focus on the impoverished population in various cities/districts in Indonesia. The data used in this study is sourced from the publication by BPS (Badan Pusat Statistika) regarding Poverty Data and Information per City/District in 2021. The method employed in this research is K-Means Clustering to group areas that exhibit high or low poverty indicators, followed by testing the value of K using the elbow method and silhouette coefficient. The final outcome of this study is the formation of three groups of cities/districts, with the last group consisting of 17 cities with the highest poverty indicators.