IMPLEMENTASI ALGORITMA APRIORI UNTUK ANALISIS TRANSAKSI PENJUALAN OBAT (STUDI KASUS : APOTEK GILDA FARMA 2 ) - Muhammad Sholihul Hamdi; I Gede Putu Wirarama Wadashwara Wirawan; Fitri Bimantoro (Program Studi Teknik Informatika, Fakultas Teknik, Universitas Mataram, 2022)
The transaction data contained in the pharmacy cannot be utilized optimally by the pharmacy. Management of data into useful information can be done by mining data (data mining). In this study, data processing uses the Apriori algorithm by utilizing association rules to obtain a pattern of connectedness from a combination of items. The results of processing transaction data will produce a number of rules that can serve as material for consideration in decision making. The amount of data used in this study was 43,191 transactions with 86,441 drug items and 449 drug names from sales transaction data for 1 year. The test scenario was carried out 4 times with 5 iterations in each test with different threshold values ​​for minimum support and confidence. The optimal minimum support threshold value obtained is 0.004 and the optimal minimum confidence is 0.06. The rules generated in this study amounted to 6 rules with 2 item combinations. The strongest rule combination is allopurinol and piroxicam with a support value of 0.006680 and a confidence of 0.191011 for the lowest rules value for a combination of paracetamol and amoxycillin drugs with a support value of 0.004396 and a confidence of 0.061809.