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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab
Yusril
Universitas Malikussaleh
Computer Science & IT Engineering

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ANALISIS SENTIMEN REVIEW APLIKASI STOCKBIT DI GOOGLE PLAY STORE DAN X(TWITTER) MENGGUNAKAN SUPPORT VECTOR MACHINE: SENTIMENT ANALYSIS OF STOCKBIT APPLICATION REVIEWS ON GOOGLE PLAY STORE AND X (TWITTER) USING SUPPORT VECTOR MACHINE Yusril; Wahyu Fuadi; Yesy Afrillia
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6446

Abstract

This study examines sentiment analysis of user reviews of the Stockbit application obtained from the Google Play Store and platform X (formerly Twitter). The aim of this research is to classify user opinions into two sentiment categories: positive and negative, using the Support Vector Machine (SVM) method. A total of 3,000 review data points were used in this study, consisting of 2,100 training data points and 900 test data points (stratified split with a 70:30 ratio) to ensure balanced sentiment distribution. The research process includes text preprocessing, feature weighting using Term Frequency-Inverse Document Frequency (TF-IDF), sentiment classification with the SVM algorithm, and model performance evaluation. Based on the evaluation results, the SVM model demonstrated high performance with an accuracy of 95.5%, precision of 93.5%, recall of 97.4%, and an F1-score of 95.3%. Although its accuracy is lower than that of Maulana et al.'s (2024) study, which achieved 99.50% on the Pluang application, this research excels in using data from two different platforms and evaluating class imbalance, making the analysis results more representative of real-world conditions. These findings indicate that SVM remains an effective method for text-based sentiment analysis in digital financial service applications.
Co-Authors Wahyu Fuadi Yesy Afrillia