IMPLEMENTASI DATA MINING PADA PENGATURAN DATA INVOICE DISTRIBUTOR MENGGUNAKAN ALGORITMA FP GROWTH

Abstract

A research was conducted at PT. Wyssa Artha Sejahtera a distribution company, to explore the processing of sales transaction data using the FP Growth algorithm to support better decision-making. The company deals with a large volume of daily sales transaction data involving various types of products, both wet and dry. The study aimed to extract useful information from the sales data, such as sales trends and the most popular products among customers. Data was collected through observations and direct interviews with the company owner from January 2022 to December 2022, using the proactive method for more comprehensive and accurate information. Upon collecting the sales transaction data, the research identified certain products with higher sales than others. RapidMiner software was utilized for processing the sales data, which proved to be suitable for implementing the FP Growth algorithm, especially for wet and dry product types with increasing sales transaction data. In the testing phase, RapidMiner successfully discovered item sets 1, 2, 3, and 4, along with their corresponding support and confidence values.The results of this research carry significant implications for PT. Wyssa Artha Sejahtera in making more informed decisions. By analyzing the sales transaction data, the company can devise more effective strategies to boost sales and meet customer demands. Furthermore, the findings can serve as a valuable reference for other companies in similar industries, helping to enhance their decision-making processes. The FP Growth algorithm analysis revealed that item sets 2, 3, and 4 had a minimum support of 20% and a minimum confidence of 70%.