Prediction using C4.5 Method and RFM Method for Selling Furniture

Abstract

Based on sales transaction data in Borobudur Furniture, it can be seen that customer demand for furniture can be said to be large, therefore special methods are needed to estimate sales that are most in-demand by customers in the future, and also special methods used to provide customer loyalty ratings. The method used to predict sales is the C4.5 method, while the method used to provide customer ratings based on customer loyalty is the RFM method. Through the process of data mining with the C4.5 method, it was found that the five items most in demand by customers were wardrobe, office chairs, buffet tv, guest table, and sofa set. While using RapidMiner as a test, the precision results are 63.64%, 89.36% for recall and 60.81% for accuracy. While through the RFM analysis process that has been carried out, there are four categories of customers and the minimum RFM total point is 3 points while the maximum RFM total point is 12 points.

Analysis of sales levels of pharmaceutical products by using data mining algorithm C45

“…Based on the analysis of the use of data mining with the C4.5 algorithm, it can be used in customer data sets into strategic management activities so that it can accommodate customers as long as possible properly, this C4.5 algorithm application must be included in the data set. The C45 method is used to predict furniture sales, through this process the product items that are most in demand by customers are found [24,25].…”