PENERAPAN DATA MINING DALAM MENGETAHUI POLA TRANSAKSI PEMBELIAN OBAT MENGGUNAKAN ALGORITMA APRIORI PADA APOTEK BINTANG FARMA KOTA JAMBI <i>Apriori Algorithm,</i> <i>Pharmacy,</i> <i>Medications,</i> <i>Patterns,</i> <i>RapidMiner,</i> <i>Transaction</i>

Abstract

Pharmacies are one of the places that distribute pharmaceutical supplies to the public. With fierce competition in the business world, especially in the pharmacy industry, pharmacies need to find the right strategy to improve their business progress. One way to overcome this is by maintaining the availability of various types of medicines needed by consumers. This study was conducted to predict medication purchasing transaction patterns at Bintang Farma Pharmacy in Jambi City using the Apriori Algorithm. The Apriori Algorithm was chosen because of its ability to identify associations or relationships between items in transaction datasets. This provides a more accurate picture of medication purchase transaction patterns. The data used includes medication purchase transaction data from January to May 2025, which was analyzed using manual calculations and assisted by RapidMiner software. The results of this study indicate that the Apriori algorithm is capable of determining medication purchase transaction patterns with an acceptable error rate, making it a reliable reference for maintaining medication availability and improving pharmacy services.