SENTIMENT ANALYSIS OF MYPERTAMINA APPLICATION USING SUPPORT VECTOR MACHINE AND NAÏVE BAYES ALGORITHMS

Abstract

In line with the needs of the community and the progress of the times in the advanced field of fintech, cash payments are currently considered insecure as well as ineffective and efficient. To run a non-cash or cashless transaction program currently run by the government, PT. Pertamina invites the public to use E-Payment from the My Pertamina application in collaboration with LinkAja. In this study, the sentiments of MyPertamina application users will be analyzed based on reviews on the Google Play Store. Review data will be analyzed to determine whether the review has positive, negative, or neutral sentiments. The data analysis stage is text preprocessing to change uppercase to lowercase, clearing text, separating text, taking important words, changing essential words, and labeling data into positive, negative, and neutral classes. As well as the classification and evaluation of results. This study used the Support Vector Machine (SVM) and Naïve Bayes classification methods. To evaluate the results, the confusion matrix was used to test the accuracy, precision, recall, and F1 score value. The classification results obtained the highest accuracy value for the Support Vector Machine (SVM) method, which had accuracy (68.50%), precision (70.00%), recall (69.70%), and F1 score (68.46%). Meanwhile, the Naïve Bayes method has performance with accuracy (63.00%), precision (63.90%), recall (61.34%), and F1 score (59.55%).