Implementation of Decision Tree Algorithm in Customer Recency, Frequency, Monetary, and Cost Profiling: a Case Study of Plastic Packing Industry

Abstract

This study aims to model the form of customer profile classification on companies using the C 4.5 and Random Forest algorithms to produce the best profile classification model from customers to sees a pattern of assessments of manual assessments so far. This study uses descriptive analysis method. Through classification of Customer Profiles with the Recency, Frequency, Monetary - Cost (RFM-C) model approach. After process the two models, the results obtained are the C4.5. After testing the two algorithms, the results obtained are the use of the C4.5 algorithm for companies to classify RFM-C which is expected to predict because it has higher accuracy and kappa values compared to the Random Forest algorithm. It can be concluded that the modeling of customer profile forms in companies that use the C 4.5 algorithm and random forest can produce the best profile classification model.