Clothing Product Recommendations Using the FP-Growth Algorithm in Siny.CO Stores

Abstract

In this modern era, there is so much competition in the business world, especially in the sales industry, that requires shop owners to find a strategy that can increase sales and marketing of the products they sell, one of which is by utilizing clothing sales transaction data using data mining. Data Mining is an iterative and interactive process to find new patterns or models that can be generalized for the future, useful and understandable in a very large database (massive database). In these conditions, good data processing techniques are needed, one of which is data mining techniques. One thing that can be used in this technique is to use the Association rule method using the FP-Growth algorithm, which is an algorithm that produces frequent itemsets which will later be used to determine recommendations for clothing products for the needs of the Siny.co Store. This research uses the RapidMiner Studio application to help process transaction data. This research method uses a minimum support of 20% and a minimum confidence of 80%, thereby creating 9 association rules, where these rules determine several products for recommendation at the Siny.Co Store, including Sashi, Canna, Alice, Tartan, Aruna, Cassandra, Nala, Lalita, and Acio.