Kepuasan Pengguna Layanan Shopee Food Menggunakan Algoritma Naive Bayes

Abstract

The rising growth of e-commerce in Indonesia has resulted in a shift in people's spending patterns toward online shopping. This growth triggers marketers or sellers to learn various strategies to satisfy consumers when shopping using applications, especially the Shopee online store. This study aimed to analyze the satisfaction of consumers and users of the Shopee food service application as measured in various aspects, including price and service quality. The population in this study were 33 respondents with a minimum age of 15 years and a maximum of 50 years who had to make payments at least once in the last six months on the Shopee application. The data collection technique in this study used an online questionnaire distribution using Google Forms, which was then analyzed by SPSS and classified using the Naïve Bayes (NB) algorithm with the RapidMiner tool. The analyzed data includes application display attributes or indicators, the number of drivers, price discounts, and payment methods. The results of this study indicate that all independent variables affect the dependent variable by 49.7% and simultaneously affect user satisfaction. Also, the accuracy of NB can reach 100% with 90% training and 10% testing data.