Improving the quality of education requires a comprehensive understanding of the conditions and characteristics of each educational institution. West Muna Regency has various secondary schools with diverse profiles and challenges. Until now, school grouping has often been done manually, which does not always accurately reflect the overall characteristics of the data. This study aims to cluster secondary schools in West Muna Regency using the K-Means algorithm as a clustering method to identify hidden patterns in school data, such as the number of students, teachers, staff, facilities, and location. The research method involves several stages, including data collection, method analysis, software implementation, and cluster testing. The clustering results produced three school clusters with different characteristics. Cluster 1 consists of schools with the most complete resources, Cluster 2 includes the largest number of schools with varying resources, and Cluster 3 represents schools with moderate conditions. These findings are expected to serve as a basis for formulating more targeted educational policies, ensuring equitable distribution of resources, and improving the quality of education in West Muna Regency.
