PENERAPAN ALGORITMA APRIORI PADA PENJUALAN SUKU CADANG KENDARAAN RODA DUA (Studi Kasus: Toko Prima Motor Sidomulyo)

Abstract

Prima Motor Shop is engaged in the sale of spare parts for two-wheeled vehicles with several brands of spare parts. Sales at Prima Motor Stores take place every day so that the transaction data will increase over time. However, the data is only used as an archive for the store. By using data mining the data will be reprocessed into information that can be used for the decision-making process. Transaction data is processed using association techniques using apriori algorithm. The a priori algorithm will calculate the support value of each item and find the frequent item set that meets the minimum confidence requirements. The minimum value for the support parameter is 25% and the minimum value for the confidence parameter is 50%. The results of the application of the apriori algorithm produce 13 association rules including 2 association rules for the Suzuki brand, 6 association rules for the Honda brand and 5 association rules for the Yamaha brand that meet the minimum requirements of two parameters, namely support and confidence parameters and tested using lift ratio testing to determine whether the resulting association rules are valid or invalid. The most sold item for the Suzuki brand is an item with code B01 namely Cool Starter Satria FU, for the Honda brand is an item with the code C14 namely Seal Shock Front Tiger, for the Yamaha brand is an item with code D01 namely Cool Starter Jupiter Z, Vega ZR, Mio J This can minimize the inventory vacancy of each of the most sold items of each product from the 3 parts brands.