Analisis Clustering Tingkat Buta Aksara di Indonesia Berdasarkan Provinsi Tahun 2024 Menggunakan Algoritma K-Means

Abstract

Illiteracy in Indonesia is still an important issue, although there has been a decline in recent years. This study aims to analyze the illiteracy rate in each province in Indonesia in 2024, using the K-Means algorithm to group data based on the illiteracy rate in the 15+, 15-45, and 45+ age groups. Data obtained from the Central Statistics Agency (BPS) were processed using data mining techniques, with the Elbow Method and Silhouette Analysis to optimize the number of clusters. The results of the analysis show that the provinces are divided into three clusters: low cluster (25 provinces), medium cluster (11 provinces), and high cluster (2 provinces). Provinces with high illiteracy rates are dominated by remote areas with limited access to education. Based on Pierre Bourdieu's theory of social inequality, this study provides strategic recommendations, including improving educational infrastructure, teacher training, and community-based literacy programs. With these results, the study is expected to support policy formulation to accelerate the reduction of illiteracy in Indonesia.