Sentiment Analysis of FlyGaruda Review Using Support Vector Machine and Naive Bayes Algorithm

Analisis Sentimen Ulasan FlyGaruda Menggunakan Algoritma Support Vector Machine dan Naive Bayes

Abstract

FlyGaruda is an official digital application owned by Garuda Indonesia that provides ticket booking and online check-in services for users. This study analyzed the sentiment of reviews on the Google Play Store by comparing the performance of Support Vector Machine and Multinomial Naive Bayes. The methods used include scraping, text preprocessing, extraction of the Term Frequency-Inverse Document Frequency (TF-IDF) feature, and evaluation using the Confusion Matrix. The dataset used totaled 4,790 reviews with positive, negative, and neutral categories. The results showed that both models obtained an accuracy of 82.25%. However, the Support Vector Machine produces a weighted precision of 77.66% and an F1-Score of 78.91%, better at handling data imbalances. Meanwhile, Multinomial Naive Bayes excels in computing efficiency with a training time of 0.08 seconds compared to 90.60 seconds on  the Support Vector Machine. In conclusion, although it is slower, the Support Vector Machine provides more consistent and accurate classification performance. This research contributes to the development of a machine learning-based opinion analysis system to improve the quality of aviation digital services in a sustainable manner. These findings can serve as a reference in the selection of the best algorithms between accuracy and computational speed in large text data and support data-driven decision-making in the modern air transportation industry in the current era of global sustainable digital transformation