COMPARISON OF K-MEANS AND K-MEDOIDS FOR DRUG DATA CLUSTERING - Andika, Tripa; Kurniabudi; Sharipuddin (Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran, 2025)
Abstract: Ineffective drug demand management can lead to problems such as imbalanced drug distribution, excess stock, or shortages in community health centers. To address this, data mining can be utilized to support the planning and control process of drug inventory. Clustering techniques were chosen because they are able to group drug data based on certain characteristics, thus identifying stable and unstable drug supply patterns. This study aims to group drug data at Simpang Kawat Community Health Center in Jambi City, which can be used as a reference in planning drug needs in the next period. Data grouping is divided into three categories: slow-moving, medium-moving, and fast-moving. The research data includes attributes of drug name, initial stock, receipt, inventory, usage, and final stock, with a total of 1758 data sets, which were processed using the CRISP-DM framework through the RapidMiner application. Cluster quality evaluation was carried out using the Davies-Bouldin Index (DBI). The results showed that the K-Means algorithm obtained a DBI value of 0.175, smaller than K-Medoids which obtained a value of 0.354. Because a smaller DBI value indicates better cluster quality, K-Means provides more optimal clustering results than K-Medoids. Through these clustering results, community health centers can utilize drug cluster information to support more efficient drug procurement planning, as well as reduce the risk of excess or shortage of stock. Keywords: data mining; clustering; k-means; k-medoids; davies-bouldin index