Penerapan Algoritma K-Means Dalam Mengelompokkan Ruang Pasien BPJS Untuk Menentukan Pola Perawatan Kesehatan Yang Efektif
DOI:
https://doi.org/10.29408/jit.v9i1.33079Keywords:
Analysis, Clustering, K-meansAbstract
The National Health Insurance organizer managed by BPJS Kesehatan plays a crucial role in ensuring equitable and quality healthcare services for all Indonesian citizens. The increasing number of BPJS participants encourages healthcare facilities to implement more efficient data-driven management. Fakhira Clinic in East Lombok still faces challenges in managing treatment rooms and formulating service strategies due to suboptimal use of data analysis, causing decisions to often be made reactively.This study aims to apply the K-Means clustering algorithm to group BPJS patient rooms. The K-Means algorithm is used to cluster BPJS patients based on their characteristics and service needs, such as gender, age, type of illness, visit frequency, duration of treatment, and payment method. Analysis of 500 BPJS patient data resulted in three main clusters: 217 patients with light treatment patterns, 158 patients with moderate care needs, and 125 patients with intensive care requirements. Each cluster shows different tendencies regarding age, length of hospital stay, and diagnosis. This information can be utilized as a basis for arranging treatment rooms, accelerating service processes, and formulating more effective and targeted care strategies.
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