Clustering Study Of Hospitals In Bojonegoro Based On Health Workers With K-Means And K-Medoids Methods

Background: Hospitals are institutions that provide inpatient care for the sick. In Bojonegoro, hospital services are considered adequate. However, a shortage of nurses often requires patients' families to assist with care. Objective: This research aims to compare clustering methods to find the best method that can be applied to cluster hospitals based on the type of health workers. Methods: This study uses two clustering methods, namely K-Means and K-Medoids Clustering, which are compared to determine the best method. The data source used is secondary data, which consists of the number of medical staff for each medical position, obtained from the Satu Data Bojonegoro website in 2020. Results: The K-means method proved to be the best for grouping healthcare workforce data. Its average within-cluster distance value is -6.763, the closest to zero. The K-means method resulted in 4 clusters. These include cluster_0 (3 hospitals), cluster_1 (1 hospital), cluster_2 (1 hospital), and cluster_3 (5 hospitals). Conclusion: The clustering results show that K-Means with 4 clusters is the best method, with Cluster_0 and Cluster_3 having below-average health workers, and Cluster_1 and Cluster_2 having above-average health workers, with Cluster_2 having the highest and Cluster_3 the lowest number of health workers in Bojonegoro.