Implementasi Data Mining Dengan Metode Apriori Dalam Menentukan Pola Pembelian Obat

Abstract

This research was conducted based on the large amount of pharmacy drug sales transaction data that has not been optimally utilized as a source of information to support decision-making. The study aims to implement the Apriori algorithm on drug purchase transaction data at Seruni Pharmacy to identify consumer purchasing patterns and support decision-making processes. The data used consisted of 13,068 drug sales transactions from 2021–2025, with 12,700 transactions analyzed after the preprocessing stage. The analysis process was carried out using RapidMiner with a minimum support value of 10% and a minimum confidence value of 60%. The results of the study produced 140 association rules indicating relationships between drug purchase products. One of the rules with the highest confidence value of 99% showed that consumers who purchased herbal products, medical devices, and wound care products also tended to purchase topical ointment cream simultaneously. In addition, associations were found between generic prescription drugs and limited over-the-counter drugs with confidence values of 71% and 78%, respectively. Based on these results, the Apriori algorithm was proven effective in identifying drug purchasing patterns and can be utilized to support inventory management, product arrangement, and promotional and marketing strategies at Seruni Pharmacy.