Monitoring Expiration and Beyond-Use Dates of Non-Compounded Drugs Through Rule-Based and Machine Learning Approaches

Marfuah Marfuah (Universitas Universal)
Masparudin Masparudin (Universitas Universal)
Raymond Erz Saragih (Universitas Universal)

Al'Adzkiya International of Computer Science and Information Technology Journal
Vol 7, No 1 (2026)

Public knowledge regarding the time limit for drug usage is still limited, despite existing educational efforts. Many people rely solely on the Expiration Date (ED) stated on the primary packaging, which indicates the post-production shelf life. However, once the packaging is opened, the usage limit no longer refers to the ED, but instead to the Beyond Use Date (BUD), which depends on the drug type, time, form, aroma, and color. The application of a Rule-Based Expert System, through a series of IF-THEN rules and machine learning. Three methods were used: decision tree (DT), Gaussian Naive Bayes (GBN), and K-Nearest Neighbor (KNN). The results showed that the decision tree method (95%), the GBN method (94%), and the KNN method (81%) can assist in monitoring drug use limits, minimizing losses, and enhancing user awareness and compliance to prevent medication errors.