Comparison of Clustering Methods for Health Clinic Stock Goods

Clinics are health service facilities for the community that provides medical services, but sometimes some clinics do not have the stock of goods needed by the community. This is because the inventory management system does not always update the stock of goods that are empty or piled up. The problem is certainly detrimental to the clinic and the community. Because this is done optimization on the stock of goods. One way to group goods is to use the clustering method. In this study, the author will compare the X-Means Algorithm, K-Means Algorithm, and K-Medoids Algorithm, to compare which algorithm is more optimal in grouping stock items at Health Clinic. The results obtained using 3 clusters show that the X-Means Algorithm has the smallest DBI value of 0.075. So that we can conclude that the X-Means algorithm is more optimal than the K-Means algorithm and the K-Medoids algorithm.