Rekomendasi Paket Pakaian Berdasarkan Pola Penjualan Menggunakan Algoritma Apriori - Eka Noviyanti, Agnes; Juanita, Safitri (STMIK PONTIANAK, 2024)
The global economy in 2023 is predicted to experience a recession along with declining activity in the trade sector in most countries in the world, including Indonesia. Currently, one of the retail clothing stores that provides many clothing products for women, namely Toko Alys Studio, wants to develop a sales strategy for clothing products in order to compete and increase profits. Thus, the contribution of this research is to find patterns of products sold based on a collection of sales transaction data at Toko Alys Studio using association rules with the Apriori algorithm. The purpose of this research is to provide recommendations for clothing sales patterns so that they can be used to develop the right clothing product sales strategy in order to get more significant profits than before. The data mining methodology used is CRISPM-DM, using a dataset of store sales transaction records from June to September 2021, totaling 885 data, pre-processing to modeling with the Apriori algorithm, and all processes using WEKA 3.8. This study concludes that using the Apriori algorithm with a minimum Support value of 15% and a minimum Confidence value of 50%, successfully found the best pattern recommendations for the sale of clothing products at Toko Alys Studio, namely a combination of 2 products as many as 10 types of patterns, and a combination of 3 products with 3 main types of patterns.