APPLYING CLUSTERING AND RECOMMENDATION SYSTEM FOR EFFECTIVE SUPERVISION IN CENTRAL KALIMANTAN INSPECTORATE

Abstract

This study aims to enhance the effectiveness of supervision at the Central Kalimantan Inspectorate through the application of clustering methods and recommendation systems. Clustering methods, specifically K-Means, are used to group supervision areas based on scores from various indicators, while the recommendation system provides specific improvement suggestions for each group. The results show that financial and performance supervision have high scores, with recommendations to maintain and improve existing standards, focusing on efficiency, transparency, and achieving performance targets. Compliance supervision shows variation in clustering, with recommendations for better enforcement of rules and regulations. Management supervision requires improving management and leadership effectiveness to enhance operational efficiency. Human resources supervision shows the best results, with a focus on improving employee competencies through training and development. The implementation of this technology is expected to optimize resource usage, enhance supervision effectiveness, and reduce operational costs. Clustering methods and recommendation systems provide deep insights into the condition of supervision and help the Inspectorate make more informed and strategic decisions. This study contributes to strengthening the supervision system and improving the overall performance of the Central Kalimantan Inspectorate, creating a more responsive, transparent, and accountable supervision system.