DATA MINING TECHNIQUE USING NAÏVE BAYES ALGORITHM TO PREDICT SHOPEE CONSUMER SATISFACTION AMONG MILLENNIAL GENERATION

Abstract

Shopee is one of the largest e-commerce platforms currently being used by Millennials. The use of Shopee itself makes it very easy for consumers to process transactions. Shopee itself is committed to maintaining and improving customer satisfaction so they don't switch to other competitors. However, it is undeniable that there are still many cases that can harm consumers when using the platform. With the cases that occur, it is very possible that there will be a big influence on the level of consumer satisfaction on the platform. Consumers will feel satisfied when the product or service used can meet consumer expectations. This study was made with the aim of predicting the level of consumer satisfaction of Shopee Indonesia among the Millennial Generation. This study applies data mining using the Naive Bayes Algorithm. The Naive Bayes algorithm itself is a simple probability classification that can calculate all possibilities by combining a number of combinations and the frequency of a value from the database obtained. The attributes used in conducting this research include Name, Gender, Age, Price, Performance and Efficiency, Fulfillment, Reliability, Control and Security, Responsiveness, Compensation, Contact, and Description of Satisfaction Value. In this study, the results obtained from several input attributes that create a causal relationship when classifying satisfied and dissatisfied consumers. The results obtained can provide benefits for the Shopee company in increasing customer satisfaction. After carrying out the testing process, it can be concluded that the Naive Bayes Algorithm is an algorithm that is suitable for use in the classification process for measuring Shopee Indonesia's consumer satisfaction level among the Millennial Generation, with an accuracy rate of 89.65%.