Sentiment Analysis of JNE User Perception using Naïve Bayes Classifier Algorithm

Abstract

The logistics industry is growing very rapidly. One of big industry in Indonesia is PT. Tiki Line Nugraha Ekakurir (JNE), which has been established for 29 years. This company has an extensive network in all cities in Indonesia, with service points of 1,500 locations. JNE has an application called my JNE on Google Play, which received more than 86,000 reviews and since December 2019 only got a rating of 2.4 stars out of a total rating of 5 stars. This study is obtained to analysis JNE user review data from Google Play. The reviews used in this study totaled 1,876 classified into positive and negative sentiment classes using the Naïve Bayes Classifier algorithm and word associations were also implemented. Classification with naïve bayes classifier with 90% training data and 10% test data had the best accuracy of 85.87%. Furthermore, for the text association, information is obtained that JNE users are talking about "send", "package", "courier", "good", "application", "fast", "service", "receive", "help", and "star". Whereas in the class of negative sentiment users often talk about "send", "package", "courier", "disappointed", "service", "service", "bad", "application", "severe", and "slow".

Keywords

• sentiment analysis
• word associations
• fishbone diagram
• jne
• google play
• naïve bayes classifier