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The problem examined in this study is about the user’s trust in using digital learning applications that are downloaded on playstore. Many reviews are given by the public about the application that has been downloaded on playstore. This review is very influential on their trust in using the application. The purpose of this study is to classify data according to labels and find out the best choice between the classification method and the proposed selection feature as a consideration in determining the use of digital learning applications.This study compares the classification method, the Näıve Bayes algorithm and the genetic algorithm (GA) as feature selection with the Näıve Bayes algorithm classification method and the particle swarm optimization (PSO) as feature selection to categorize the reviews in the playstore. The experimental results show that the Näıve Bayes algorithm and PSO as feature selection is the best model between the two models proposed in this study. Reviews can be classified into positive and negative labels well. The accuracy is 98.00%. The results of the classification are expected to help in making decisions when going to use digital learning application.

journal of physics. conference series

Comparison of Naïve Bayes Algorithm with Genetic Algorithm and Particle Swarm Optimization as Feature Selection for Sentiment Analysis Review of Digital Learning Application