Implementation of Clustering Method Using K-Means Algorithm for Grouping BPJS Health Patient Medical Record Data
DOI:
https://doi.org/10.30871/jaic.v9i5.10046Keywords:
K-Means Clustering, Z-score, Medical Records, BPJS Health, RSUD SimeulueAbstract
Clustering medical record data of BPJS Health patients is essential in supporting data-driven decision-making in hospitals. This study aims to implement the K-Means algorithm to cluster patient medical records at RSUD Simeulue based on BPJS class and patient address variables. The data were first normalized using the Z-Score method to standardize variable scales, followed by the iterative application of the K-Means algorithm until convergence was reached at the sixth iteration. The study employed three Cluster, namely Cluster 1 (Very Many), Cluster 2 (Many), and Cluster 3 (Not Many). The final results show that Cluster 1 contains 258 patients from Class 1 and 292 from Class 2; Cluster 2 consists of 296 patients from Class 2; and Cluster 3 includes 101 patients from Class 1, 115 from Class 2, and 148 from Class 3. In addition to classification by BPJS class, clustering based on patient address revealed a dominant distribution from Simeulue Timur, Teluk Dalam, and Teupah Selatan sub-districts. The clustering results were implemented into a web-based information system using the Laravel framework and MySQL database, enabling hospital administrators to visualize and analyze patient data effectively. This study demonstrates that the K-Means algorithm can be effectively applied in classifying medical record data to support healthcare management decision-making.
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