Grouping of General Hospitals Based on Specialist Doctors in the Special Region of Yogyakarta with the K-Means Clustering and Visualization on the Dashboard Tableau
Keywords:
hospitals, K-Means, specialist doctor, TableauAbstract
One of the most needed health workers in hospitals is a specialist doctor. The number of general hospitals in Special Region of Yogyakarta (DIY) is 55, with different characteristics of specialist doctors. Purpose: Information on the grouping of general hospitals based on the characteristics of specialist doctors will help provide information about the distribution of health workers. Methods: This study uses K-Means Clustering as a non-hierarchical clustering method that can group large amounts of data with fast and efficient computation time and is easy to adapt. Results: This study used the number of clusters (k) 2, 3, 4, and 5 and then compared them. Based on the largest silhouette index coefficient, the best number of clusters is k = 2. A total of 35 hospitals in cluster 2 represent hospitals with an adequate number of specialists. A total of 2 hospitals in cluster 2 represent hospitals with a sufficient number of specialists. Visualization using the Tableau dashboard has also provided benefits, namely providing information on the number of specialist doctors in each general hospital, cluster results, and cluster profiling. Implications: The K-Means method can provide a reference for the distribution of specialist doctors in the Special Region of Yogyakarta.
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