Sentiment Analysis of the Skyscanner Application on Google Play Store with a Comparison of Naive Bayes and Support Vector Machines

Triana, Laely
Saputro, Wahju Tjahjo
Chirzah, Dewi

Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE)
Vol 7, No 1 (2026)

The digital world is growing rapidly and has a significant impact on the tourism sector. Therefore, technology must adapt to developments to meet human needs. Travel booking services such as Skyscanner allow users to book flights, accommodation, and transportation online through the app. With the large number of Skyscanner user reviews on the Google Play Store. The majority of data reviews use Indonesian languanges; sentiment analysis is needed to determine user sentiment towards the app. This study aims to analyze user sentiment towards the Skyscanner app using collected user comment review data. The data is then classified into two sentiment classes: positive and negative. The classification results using a comparison of two algorithms, Naive Bayes and Support Vector Machine, SVM produced a higher accuracy of 89.74%. Naive Bayes achieves lower accuracy 82.08% than SVM. This concludes that the SVM algorithm is more effective in producing optimal classification accuracy than its comparison algorithm, Naive Bayes.