Data Mining for Drug Inventory Using Web-Based FP-Growth Method
DOI:
https://doi.org/10.35335/jict.v16i2.263Keywords:
Association Rules, Data Mining, FP-Growth Algorithm, Inventory Management, Pharmacy SystemAbstract
Pharmacy inventory management plays a critical role in ensuring product availability and preventing financial losses due to overstock or stock shortages. However, many pharmacies, including Global Medcare Pharmacy in Medan, still experience challenges in maintaining optimal inventory levels due to the absence of data-driven management systems. This study aims to develop and implement a website-based data mining application using the FP-Growth algorithm to identify frequent itemsets and uncover association patterns within pharmaceutical sales transactions. The FP-Growth algorithm was applied to 300 transaction records to generate frequent item combinations with a minimum support threshold of 3%. The results reveal strong associations among specific drugs, such as Amlodipine 10mg, Azithromycin 500mg, and Cetirizine 10mg, with confidence levels reaching up to 100%. These findings demonstrate that FP-Growth effectively identifies purchasing patterns that can guide pharmacies in forecasting demand, managing stock levels, and designing promotional bundles. The practical implication of this research is that integrating FP-Growth into pharmacy information systems can enhance decision-making accuracy, improve service quality, and increase operational efficiency. Nevertheless, the study is limited to a single-site dataset and static analysis; future research should employ larger datasets and hybrid predictive approaches for real-time implementation across multiple pharmacy networks.
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