Penerapan Algoritma Apriori Untuk Menemukan Pola Asosiasi pada Data Penjualan Toko Buku

This study applies the Apriori algorithm in transaction analysis and sales in a bookstore setting to identify product relationships that are frequently purchased at the same time. The dataset analyzed consists of 2.470 customer transactions from Kaggle and was processed using RapidMiner with a minimum support parameter of 0.1 and confidence of 0.6. The data used are binary transaction data indicating whether certain product categories were purchased or not, such as book accessories, stationery, children’s books, fiction books, magazines, and novels. The analysis process was carried out through several stages, including data preprocessing, transforming the data into transaction format, and applying association methods to discover relationships among products that are often bought together. The results show that certain product combinations have a strong tendency to be purchased together, so they can be used as a basis for sales strategies, product arrangement, and more effective promotions. Thus, the use of RapidMiner in processing transaction data can help generate useful information for decision-making in the sales field.