Enhancing Decision-Making in Local Government through K-Means Clustering of Structural Official’s Performance

Employee performance evaluation is a critical process in public sector management. However, in Karanganyar Regency, this process has been traditionally conducted on an individual basis, leading to inefficiencies and a lack of actionable insights. This study addresses the gap by applying the K-Means clustering algorithm to categorize the performance of structural officials based on 2021 Employee Performance Target (SKP) data. Key performance indicators include SKP Value, Service Orientation, Commitment, Cooperation, Leadership, and Work Initiative. Using RapidMiner, the data was clustered into three categories: “very good,” “good,” and “satisfactory.” The clustering quality was validated using the Davies-Bouldin Index (DBI), achieving an optimal value of 0.113, which indicates high intra-cluster similarity. The results provide a data- driven foundation for more efficient performance assessments, aiding decision-making in promotions and personnel management. This study demonstrates the potential of machine learning, specifically K-Means clustering, in improving administrative processes and strategic decision-making within local government.