Track: Data Analytics and Big Data

Abstract

This study aims to carry out purchase patterns using the Frequent Pattern Growth Algorithm at 212 Mart Pekanbaru. As modern retail, 212 Mart offers a wide selection of everyday things. Due to its proximity to other stores, 212 Mart is under pressure to maximize its chances of attracting and keeping customers. Every day, there are many transactions at 212 Mart. Data mining turns mountains of incompletely processed sales transaction data into knowledge that can be considered when making decisions. FP-Growth uses Association Rules and its algorithm to identify the data set that frequently co-occurs from the data set that generates consumer buying patterns. RapidMiner software is used to process the data, and 28 rules with more than 20% support and confidence more significant than 90% are produced. The outcomes of the association rules are utilized to choose which product proposals, product layouts, and promotional brochures should be replenished simultaneously