Implementasi Metode K-Means untuk Rekomendasi Penerima Kartu Indonesia Sehat Desa Katur

Abstract

The Healthy Indonesia Card is health insurance that is subsidized by the government for underprivileged people, but the provision of the Healthy Indonesia Card is not evenly distributed, because the selection of recipients of this KIS assistance is done manually. The government must provide adequate health facilities for underprivileged people with health insurance that is right on target. Therefore, this research is a solution for village governments in sorting KIS recipient participants automatically using a system, so that KIS distribution can be even and on target. The method used in this research is the K-Means algorithm which can group residents to recommend recipients of KIS assistance in Katur village. And implementing the K-Means method allows structured processing, resulting in accurate and efficient recommendations. There are 150 data from Katur village residents. And the calculation results from this system, cluster 1 has 90 residents and cluster 2 has 60 residents. With the provisions, cluster 1 is the priority that gets KIS. The conclusion of this research is that the application of the KMeans Clustering algorithm to the recommendation system for Healthy Indonesia Card recipients produces an accuracy of 89.3% and is proven to be effective. This implementation was successful in finding residents who were worthy of receiving KIS assistance.