Implementasi Metode K-Means Clustering dan Association Rules Apriori untuk Pengelolaan Stok Obat
DOI:
https://doi.org/10.37859/jf.v16i2.12088
Abstract
Drug inventory management at Klinik Pratama Kencana was previously performed through manual or semi-manual recording, making stock monitoring and procurement decisions less effective. This study develops a web-based drug inventory management system by integrating K-Means Clustering and Apriori Association Rules. Historical stock and drug-out transaction data from January to March 2026 were processed using a descriptive quantitative applied-research approach. K-Means used initial stock, incoming stock, outgoing stock, and remaining stock attributes after Min-Max normalization to classify 64 drugs into fast-moving, medium-moving, and slow-moving groups. Apriori analyzed 597 transaction baskets with a minimum support of 5% and a minimum confidence of 40%. The clustering produced 15 fast-moving, 27 medium-moving, and 22 slow-moving drugs. Apriori generated several strong rules; the rule Acetylcysteine to Ambroxol achieved 11.06% support, 85.71% confidence, and a lift ratio of 5.39. System calculations matched Microsoft Excel validation results, all tested functions were valid, and five users gave a 96% acceptance score. The integrated system provides stock status, restock recommendations, monthly reports, and co-occurrence patterns to support more structured inventory decisions. By combining stock movement classification with drug co-occurrence patterns, the system helps administrators prioritize restocking together with related medicines, thereby reducing manual checking and improving inventory monitoring efficiency.
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