Implementasi Data Mining Dalam Menentukan Pola Pembelian Obat Menggunakan Metode Apriori

Abstract

The fierce competition in the pharmacy industry requires sellers to continue to improve their sales strategies to increase sales of medicines. The availability of different types of medicines that consumers need is one step in overcoming this. This research uses an a priori algorithm to determine drug purchasing patterns. By using a priori algorithms in pharmacies, a system can be created to determine drug purchasing patterns, which is useful in determining drug purchasing targets well and can improve sales strategies. The data studied are one year's retail and wholesale transaction data. The pattern of drug purchasing associations obtained with a minimum support of 5% and a minimum confidence of 60% produces 8 association rules.The association rule with the highest  confidence of 96.1% is that if consumers buy pseudoephedrine 30 mg and amoxicillin trihydrate 500 mg, they will also buy paracetamol 500 mg. Drug types that meet the minimum support and minimum confidence are Pseudoephedrine 30mg, Amoxicillin Trihydrate 500mg, Mefenamic Acid 500mg, Prednisone Triman 5mg pot, Cetirizine Hcl 10mg, Cefadroxil Monohydrate 500mg and Paracetamol 500mg.