Sentiment Analysis of the Indriver Online Ojek Application using the Naïve Bayes Classifier Method

According to statista.com, there are 73.1 million online motorcycle taxi users in Indonesia and there are 68.1 million active online motorcycle taxi users in Indonesia especially in the province of North Sumatra, there are 43,811 online motorcycle taxi drivers. The Indriver online motorcycle taxi application is an international online transportation service that gives passengers and drivers the freedom to negotiate prices. Sentiment analysis analizes text to determine positive, negative, or neutral sentiments. The method commonly used in sentiment analysis is the Naive Bayes Classifier method. This research uses quantitative methods to analyze sentiment toward the InDriver online motorcycle taxi application by utilizing the Naïve Bayes Classifier algorithm. User review data is collected from reviews on the Google Play Store, then cleaned and converted into a format suitable for statistical analysis. To analyze sentiment towards the InDriver online motorcycle taxi application using the Naïve Bayes Classifier method, collecting review data and user comments using the Python library and the Visual Studio code application, carrying out preprocessing, TF-IDF weighting, dividing the data into 70% and 30%, after that conducting testing using naïve Bayes classifier algorithm, as well as carrying out evaluation using a confusion matrix. The results of calculating the level of accuracy using the Naïve Bayes method for sentiment classification can be said to be good, this can be seen from the accuracy results on a dataset of 1393 with a comparison of training data and test data of 7:3, obtaining an accuracy value of 76%, precision of 71% , recall of 81% and f1-score of 76%. The results of this research analysis produced superior positive sentiment totaling 677 and negative sentiment totaling 608 while neutral was 90