Drug Data Clustering Based on Total Inventory and Total Demand for Drugs Using the K-means Clustering Method at Pajar Bulan Health Center - Robi Saputra; Liza Yulianti; Lena Elfianty (Penerbit Jurnal Indonesia, 2022)
At Pajar Bulan Health Center, drug supply data processing is already using office application packages, namely Microsoft Word and Excel. The application package is used for making monthly usage reports, requests and drug supplies. Constraints that often occur are that it takes a long time to manage drug inventory data because they have to record one by one the amount of drug use and the number of drug requests to be made. The number of requests is carried out every month by looking at the latest drug supply, if the stock starts to run low then a request is made. However, it is possible that the stock has run out before making a request, this results in a lack of drug supply management. Drug data clustering is carried out based on the amount of supply and the number of requests for drugs at the Pajar Bulan Health Center through the K-Means Clustering Method approach. To help cluster the drug data, an application was built using the Visual Basic .Net programming language and SQL Server 2008r2 database. Clustering of drug data is carried out in units of pcs in 2021 where the amount of inventory is reduced by the number of requests for drugs, so that the results obtained are 2 drugs enter cluster I and 27 drugs enter cluster II. good and the application can help the Pajar Bulan Health Center in knowing the grouping of drug data based on 2 groups, namely the few clusters and the large clusters