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All Journal Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika)
Chofifah, Zuli
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Computer Science & IT Decision Sciences, Operations Research & Management

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Komparasi Metode SVM Dan Random Forest Pada Analisis Sentimen Ulasan Aplikasi Open AI Chofifah, Zuli; Wakhidah, Nur
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 10, No 2 (2025): Edisi Agustus
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v10i2.925

Abstract

In this study, the sentiment analysis of ChatGPT application reviews gathered from the Google Play Store is compared using the Support Vector Machine (SVM) and Random Forest techniques. The google-play-scraper package was used to scrape for the dataset. The data was subjected to a number of preparation procedures before categorization, such as text normalization, stopword removal, character removal, and stemming using the Sastrawi package. After classifying each review using both algorithms, the sentiment of each review was labeled according to its rating score. According to the experimental results, Random Forest attained an accuracy of 94.00%, whereas SVM achieved 95.00%. According to these results, SVM performs marginally better than Random Forest at identifying the sentiment of user reviews of OpenAI applications.
Co-Authors Nur Wakhidah