Penerapan Algoritma K-Means Clustering Untuk Data Obat - Tis Asy Aria; Yuliadi Yuliadi; M Julkarnain; Fahri Hamdani (KLIK: Kajian Ilmiah Informatika dan Komputer, 2023)
In the process of planning and controlling the supply of UPT drug stock. Unter Iwes Sumbawa Community Health Center is not yet optimal because the problem of inaccuracy in analyzing drug needs affects the amount needed and demand for medicine. In this study, drug data analysis was conducted by applying the K-Means Clustering algorithm. Drug data is grouped based on usage levels C1, C2, and C3 (low, medium, high) with attributes namely Drug Name, Unit, Supply, Demand and Usage. The results of this study can provide a strategy as a reference for future drug planning and needs. From the results of drug data testing from January to December 2022 with a total of 4,642 data and training data totaling 4,622. The results of the performance of grouping drugs using the Davies Bouldin method of 0.513 clusters of C1/C0 (low) data were 4433 items with 4000 uses. C2/C3 cluster (medium) is 184 items with 10,850 drug use and C3/C2 cluster (high) is 5 items with