ANALISA POLA PEMBELIAN KEBUTUHAN IBU DAN BAYI UNTUK MEMBUAT PAKET BELANJA MURAH DI TOKO LOVE BABY STORE - Zai, Onesimo; Harman, Rika (Computer Science and Industrial Engineering, 2022)
Competition in the business sector is a common thing and cannot be avoided. And that also applies to every business entity that is run in the online marketplace. The purpose of this study is to (1) apply data mining with association rules in managing sales data at the Love Baby Store store to produce shopping item sets that can help consumers in the check out process (2) make cheap shopping item sets from the results of sales data analysis previously to find out consumer buying patterns (3) Applying the FP-Growth algorithm, which is one of the algorithms in data mining to determine data that often appears, so that it can get information about consumer patterns and tendencies in shopping. (4) Love Baby store can find out consumer buying patterns, so they can develop new business strategies, such as designing cheap shopping item sets that can make it easier for consumers to check out and at a more affordable price. This research is a research that uses data mining in the form of sales transaction data for 3 months. This data was analyzed using FP-Growth. The results of this study are the number of purchase intentions is influenced by several factors, namely the relatively cheaper price and the existence of a discount or free shipping promo. The purchase pattern is designed using the FP-Growth method with the result "If consumers buy the Zap Jip Off Road 4 Drive Inertial Baby Toy Car, they will also buy a Fruit Teether/Baby Tooth Teether [Without Packing]" with a confidence level of 67%