Customer Loyalty Classification with RFM and Naïve Bayes for Decision Making in Indonesia E-Commerce Industry

: The problem faced by the e-commerce industry in determining customer loyalty is that it is challenging to be classified because to set strategy in every year the company should define customers who are feasible in terms of loyalty to the company. The differentiator in this study uses Naive Bayes as a classification method in detail to the attributes that are tested and the customer is classified by the RFM method and in previous studies that have been conducted by other researchers are still little discussing the combining of these two methods between Naive Bayes and RFM, then positioning in this research between ecommerce business actors, the business competition to get customer loyalty is very important as a basis for taking appropriate decision making for stakeholders. Then the result from Naive Bayes is 62% feasible and not feasible 38% then assisted by RFM method as data analysis to each customer based on segmentation use ”usage rate” attribute on data so that with processed data can make an essential reference in making decisions.