PENGGUNAAN K-MEANS DAN HIERARCHICAL CLUSTERING SINGLE LINKAGE DALAM PENGELOMPOKKAN STOK OBAT

Sibarani, Michael Alexander Justin Audison
Diyasa, I Gede Susrama Mas
Sugiarto, Sugiarto

Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika
Vol. 5 No. 2 (2024): Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistik

Adequate and efficient availability of medicines is necessary to ensure patients receive optimal care. However, inefficient drug stock management can result in various problems, such as waste of resources, lack of necessary drugs, or even excessive stock. This study aims to improve the efficiency of the drug stock management process by using KMeans Clustering and Hierarchical Clustering methods on drug stock data. The data used includes information on initial stock, purchase, incoming distribution, service, outgoing distribution, outgoing adjustment, and final stock. Clustering analysis was performed to identify patterns in the drug stock data, which was then validated using Silhouette Score. The results showed that Hierarchical Clustering was able to achieve a Silhouette Score of 0.976, while KMeans achieved a Silhouette Score of 0.954