Clustering of Inpatients at USU Hospital Using The K-means Algorithm
DOI:
https://doi.org/10.55537/cosie.v3i2.768Keywords:
Clustering, K-Means, Rapid MinerAbstract
This study aims to identify patterns of differences in the characteristics of inpatients at the North Sumatra University Hospital (USU Hospital) based on Social Security Administering Body (BPJS) class using the k-means algorithm approach. The data used in this study involves the number of patients with different BPJS classes during a certain period. The data analysis method uses the k-means algorithm to form groups of similar patients based on relevant variables, such as diagnosis, responsible doctor, and insurer. It is hoped that the results of the cluster analysis will provide a deeper understanding of the characteristic patterns of inpatients with different BPJS classes at USU Hospital. The findings from this research can provide valuable information for hospitals and the Social Security Administration in managing resources, planning health services, and improving service quality. In addition, this research can also provide a basis for the development of more effective personalized patient care strategies according to the needs of specific patient groups. By using this approach, it is hoped that this research can make a positive contribution to the efficient management of health resources, improve the quality of health services, and provide a basis for further research in the context of cluster analysis of inpatients with different health insurance groups
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