Klasterisasi Pasien Rawat Inap BPJS pada RS Islam Assyifa Sukabumi menggunakan Metode K-Means - Sjamsuddin, Irfan Nafis; Firmansyah, Dasya Arief; Laferani, Yuni (Jurnal Kridatama Sains dan Teknologi, 2025)
Hospitals as providers of quality health services face challenges in managing increasingly complex patient data. The data has not been optimally utilized by hospital management and has great potential to be analyzed and become a basis for decision-making. Optimization can be utilized by BPJS inpatient data at the Assyifa Sukabumi Islamic Hospital in the fourth quarter of 2024 using data mining techniques. The technique proposed in this study is the K-Means method to group BPJS patients based on certain variables such as age, gender, disease diagnosis, inpatient class, and length of hospitalization. The results of this study revealed that there were 3 clusters of 3526 patient data. Cluster 1 consists of 1545 patients with infectious diseases caused by microorganisms. Cluster 2 consists of 712 patients with diseases related to pregnancy, childbirth, or symptoms that must be identified through further clinical or laboratory examinations. Cluster 3 consists of 1269 patients with diseases associated with the respiratory system, digestive system, and blood circulation system. The evaluation showed that the grouping of BPJS patients with 3 cluster results had the best quality, with a Davies-Bouldin Index (DBI) value of 0.561. The study results can be a reference in planning the allocation of hospital resources. Suggestions for further research are the application of other data mining techniques in optimizing hospital data management