Penerapan Data Mining dalam Menentukan Produk Penjualan Terlaris Menggunakan Algoritma Apriori

Erza Muhammad Randi; Rima Tamara Aldisa

doi:10.47065/josyc.v5i4.5820

Erza Muhammad Randi Universitas Nasional, Depok, Indonesia
Rima Tamara Aldisa * Universitas Nasional, Depok, Indonesia

(*) Corresponding Author

DOI: https://doi.org/10.47065/josyc.v5i4.5820

Keywords: Data Mining; Apriori Algorithm; Association Rules; Tansaction Analysis; Stock Management

Abstract

Information regarding best-selling product data is something that is important to know when analyzing a store's business. Associations between best-selling products can provide useful information for increasing sales profits. However, in reality, the Mabestie Bouquet Store experiences limited problems in analyzing data on best-selling products and their associations using manual calculations only. Therefore, this research aims to create a website for calculating data analysis of best-selling products and their associations using a priori data mining algorithms. The information obtained is data on best-selling products and associations between products in order to develop business strategy progress in the store so that you can know for sure which products need to increase stock and the associations between products that are often purchased by customers. Stock availability of goods that is not managed well has an impact on the shop, for example if goods run out when consumer demand is high then there will be no purchases and this will reduce shop profits. Data mining is the process of utilizing and processing data to find patterns or related relationships in large data sets, and this technique has been widely applied in various fields, including the sale of bouquet products. By using data mining, stores can identify customer preferences through in-depth analysis of complex bouquet sales data. This research focuses on using the Apriori algorithm to analyze sales transaction data at the Mabestie Bouquet Shop. The Apriori algorithm, as a method of association rules, is used to determine combination patterns of itemsets and association rules systematically and accurately. The analysis results show that the items with the highest support value are the Birthday Bouquet Topper and Original Silverqueen Bouquet with a value of 13.3%, and the highest confidence value with a value of 52.6%. Based on this data, it is known that the pattern of purchasing the best-selling product is that if a customer buys a Birthday Bouquet Topper, they will also buy an Original Silverqueen Bouquet product. Based on this data, these two products are products that must be provided with more optimal stock in order to increase sales profits. These findings provide important insights into customer preferences and bouquet purchasing patterns, which can be developed to design more effective marketing strategies and increase the effectiveness of promotions and in-store sales.

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