APPLYING K-MEANS CLUSTERING FOR GROUPING PAPUA’S DISTRICTS BASED ON POVERTY INDICATORS ANALYSIS

Abstract

In the context of Indonesia's resource-rich development, poverty remains a major challenge, especially in Papua Province which has the highest poverty rate. Although Papua is rich in resources such as minerals, tropical forests, and biodiversity, challenges such as economic inequality, lack of infrastructure, and social conflict hinder economic and social progress. This research aims to implement the K-Means Clustering algorithm to cluster districts/cities in Papua based on poverty indicators, including the percentage of poor people, poverty line, average years of schooling, human development index, poverty depth index, poverty severity index, unemployment rate, and per capita expenditure. The research methodology includes data collection from the Central Statistical Agency (BPS), data processing through cleaning and transformation stages, and application of K-Means Clustering to determine the optimal cluster using the elbow method and silhouette score. The results show that the districts/cities in Papua can be grouped into two main clusters: C0, which indicates high poverty rates and C1, which indicates low poverty rates. This research is expected to provide a strategic foundation for the government to design more focused and effective development policies in reducing poverty in Papua.