Abstract—The present study reports the findings of a comparative analysis conducted on health facility research (HFR) data, based on a clustering method with K-Means and K-Medoids algorithm. The K-Means algorithm consist of four steps: specifyng the centroid values, grouping data on the centroid, calculating centroid values, repeating grouping data on the centroid, and calculating the values until the cluster is stable (convergence), The K-Medoids algorithm consists of six steps: medoids initialization, data allocation to the nearest medoid, determinination of new medoids, calculation of data distance with medoid, calculation of deviation, and repeating the determination of new medoids, until the deviation to the convergence cluster is counted. The data utilized in this study are HFR data located in the Jakarta district and surrounding areas, from 2013 to 2018. Our results showed that the execution time from K-Medoids algorithm outperformed the K-Means algorithm. The K-Medoids algorithm speeded up the execution time, and at the same time improved the density value between clusters (silhouette). By utilizing the clustering method, health facilities in hospitals in Jabodetabek can be categorized based on their resources.
Index Terms—Clustering, K-Means, K-Medoids, health facility research.
The authors are with the Department of Informatics, Faculty of Computer Science, Mercu Buana Univerisity, Jakarta 11650, Indonesia (e-mail: email@example.com, firstname.lastname@example.org).
Cite: Yogi Wahyu Romadon and Devi Fitrianah, "The Comparative Study on Clustering Method Using Hospital Facility Data in Jakarta District and Surrounding Areas," International Journal of Machine Learning and Computing vol. 9, no. 6, pp. 749-755, 2019.Copyright © 2019 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).