Analisis Sentimen pada Aplikasi Translate Google Menggunakan Metode SVM (Studi Kasus: Komentar Pada Playstore)

Sri Ayu Ashari (Universitas Negeri Gorontalo)
Muhammad Wahyu Ade Saputra (Magister Teknik Informatika Universitas Amikom Yogyakarta)
Esta Larosa (Universitas Negeri Gorontalo)
Bait Syaiful Rijal (Universitas Negeri Gorontalo)

Jurnal Teknik
Vol 21 No 2 (2023): Jurnal Teknik

This research aims to analyze reviews in understanding the opinions and emotions expressed by users regarding the Google Translate application on the Google Play Store using sentiment analysis. By using the Support Vector Machine (SVM) method in sentiment analysis of the Google Translate application, to get a better understanding of how users respond to the application. This can help developers improve the application experience, responding better to user needs and preferences. This user review analysis uses the SVM method. The measuring tool in this research uses, firstly, the Indonesian Lexicon as a tool to obtain positive and negative results, secondly, term frequency–inverse document frequency (tf-idf) as a support for the results of the evaluation. Google Translate app has a dataset of 1000 user reviews collected from Google play store. The results of analysis using Support Vector Machine produced 95% accuracy, with "no" as the result of the most positive and negative reviews out of 1580 reviews.