Penerapan Algoritma Apriori Untuk Rekomendasi Penjualan Paket Lipstik

Abstract

The cosmetic industry has undergone significant changes during the COVID-19 pandemic, with consumers preferring skin care products over decorative products such as powder, lipstick, and perfume. It is a challenge for PT Titian Citra Kharisma, a business company engaged in the cosmetics sector, to be agile in determining the right strategy to survive. At this time, the problem is that all sales transactions of Kamalia Lip matte lipstick are stored neatly in Microsoft Excel documents and have not been processed into helpful information for the company to determine the right strategy to increase sales turnover. So this study applies the association rule method with the Apriori algorithm using a collection of lipstick sales data on Kamalia Lip Matte products to find information in the form of recommendations for selling lipstick packages according to the most popular variants demanded consumers. This study aims to find and provide recommendations for lipstick packages to increase sales of Kamalia Lip matte products and assist in product stock management. This research uses Weka software. The Apriori algorithm uses a minimum support value of 0.04 and a minimum confidence value of 0.27. This study concludes that using the a priori algorithm, information is found in the form of recommendations for selling lipstick packages from various existing Kamalia Lip matte variants, where there are 2 (two) combinations of items that are often purchased simultaneously by customers, namely: with the confidence of 28.16% when buying soft pink variant, they will buy the soft brown variant; and with a confidence of 31.10%, if you buy the fierce red variant, you will buy the soft pink variant.