Prediction using C4.5 Method and RFM Method for Selling Furniture
Ifan Nugroho Budi1, Indra Ranggadara2, Ifan Prihandi3, Nia Rahma Kurnianda4, Suhendra5
1Ifan Nugroho Budi*, Faculty of Computer Science, Mercu Buana University, Jakarta, Indonesia.
2Indra Ranggadara, Faculty of Computer Science, Mercu Buana University, Jakarta, Indonesia.
3Ifan Prihandi, Faculty of Computer Science, Mercu Buana University, Jakarta, Indonesia.
4Nia Rahma Kurnianda, Faculty of Computer Science, Mercu Buana University, Jakarta, Indonesia.
5Suhendra, Faculty of Computer Science, Mercu Buana University, Jakarta, Indonesia.
Manuscript received on September 16, 2019. | Revised Manuscript received on October 05, 2019. | Manuscript published on October 30, 2019. | PP: 535-541 | Volume-9 Issue-1, October 2019 | Retrieval Number: A9665109119/2019©BEIESP | DOI: 10.35940/ijeat.A9665.109119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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 Rapid Miner 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.
Keywords: C4.5, Customer Segmentation, RFM, Sales Prediction.