Penerapan Data Mining Untuk Menganalisis Penjualan Barang Dengan Menggunakan Metode Apriori Pada Foodmart Supermarket Semanggi

Abstract

Foodmart Supermarket Semanggi is one of the retail companies located in South Jakarta, with increasingly fierce business competition forcing companies to look for new breakthroughs to determine the right strategy in their business operations. The placement of products that are not in accordance with consumer behavior in buying goods at one time can affect the level of sales, over time sales transaction data will continue to grow and only be stored as an archive without being used properly, where basically data is a collection of useful information. Based on these problems, a system is needed to manage sales transaction data, where sales transaction data can be used by company management to analyze consumer spending patterns using a priori algorithm data mining techniques. The application of a priori algorithm aims to find shopping patterns for products that are often purchased simultaneously in an adjacent area as well as to develop the right sales strategy to generate maximum profit in accordance with the company's goals. The result of this study is in the form of a consumer shopping pattern finder system with an a priori algorithm method that can be used as an alternative to decision making in the placement of goods that are close to each other according to consumer shopping behavior.