Implementasi Data Mining untuk Menentukan Pola Pembelian Obat Menggunakan Metode Apriori - Muhtajuddin Danny; Isarianto Isarianto (Bulletin of Computer Science Research, 2026)
The development of information technology has increased the amount of drug sales transaction data in the pharmacy sector. However, transaction data are generally used only as administrative archives and have not been optimally utilized to produce strategic information. This study aims to implement data mining using the Apriori method to determine drug purchasing patterns based on pharmaceutical transaction data. This research employed a quantitative approach using the Pharmacy Transactional Dataset obtained from the Kaggle platform. The research stages were conducted using the Cross Industry Standard Process for Data Mining (CRISP-DM), including business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The analysis process was carried out using the Python programming language with the assistance of the pandas and mlxtend libraries. The results showed that the purchasing relationship between Paracetamol and Vitamin C had the highest association value with a support value of 32% and a confidence value of 78%. These results indicate that the Apriori algorithm is capable of identifying relationships among drug products based on pharmaceutical transaction data. The resulting information can be utilized to support promotional strategies, drug inventory management, and business decision-making in the pharmaceutical sector.