Sales Level Analysis Using the Association Method With the Apriori Algorithm

Abstract

The company does not yet know the pattern of consumer purchases because so far, the sales transaction data has not been used correctly and does not have a unique method to determine consumer buying patterns. To overcome the problems on the company, this research was done to reprocess sales transaction data for 2018-2019 using data mining techniques with association methods and apriori algorithms. RapidMiner is a supporting application used to find association rules derived from transaction data. Processed transaction data using the Knowledge Discovery in Database (KDD) approach. Thus, the company can determine consumer habits in buying goods derived from sales transaction data for 2018-2019. The results of this study are that in 2018, nine association rules were obtained, of which the best were CT G-246 ⇒ CT G-250 and CT G-250 ⇒ CT G-246. In 2019, nineteen association rules were obtained, of which the best were PN 0441, SK 0175 ⇒ SK 0530 and SK 0175, SK 0283 ⇒ SK 0530. From the best association rules, the goods in the Coat (imported), Pants, and Skirt categories are categories that are often bought together.

Implementation of the Association Method in the Analysis of Sales Data from Manufacturing Companies

“…Companies usually only use transaction data to view stock, calculate the number of sales and calculate monthly profit (Rahmawati & Merlina, 2018). Sales transaction data is mostly only collected and archived without analysis on sales transaction data because the data that is growing increasingly from time to time makes it difficult for companies to utilize the data and not know what to do with it (Samuel, Sani, Budiyantara, Ivone, & Frieyadie, 2022). Use of sales transaction data by analyzing the behavior and habits of customers who often buy instant cement products.…”