Evaluation Of Fuzzy C-Means Method For District Clustering

This study analyses the use of Fuzzy C-Means algorithm to cluster districts in South Sulawesi based on the education level of the population. Two distinct groups were found with several districts falling into each group after 17 iterations to reach the optimal solution. The clustering results were visualised with a point spread graph. The Fuzzy C-Means algorithm was executed using Python with certain parameters. The research aims to improve the quality of education with proper resource allocation and identification of districts based on the highest education. The data used includes education indicators and district minimum wage. The results are expected to provide input for a more targeted education policy in South Sulawesi. Fuzzy C-Means algorithm is effective for analysing and clustering education data in education policy decision making.