Sentiment Analysis of SpeedCash Application Reviews on Google Play Store Using Naïve Bayes and Support Vector Machine Methods
Analisis Sentimen Ulasan Aplikasi SpeedCash di Google Play Store Menggunakan Metode Naive Bayes dan Support Vector Machine
DOI:
https://doi.org/10.21070/ups.12450Keywords:
Speedcash, Naïve Bayes, SVM, Sentiment Analysis, E WalletAbstract
The rapid development of digital financial services in Indonesia has increased the use of electronic payment applications such as SpeedCash. User reviews on the Google Play Store provide important information for evaluating service quality and satisfaction. This study aims to analyze user sentiment toward SpeedCash and compare the performance of Naïve Bayes and Support Vector Machine (SVM) algorithms. Data were collected through web scraping, resulting in 30,096 raw reviews and 24,922 valid reviews after preprocessing, including case folding, cleaning, tokenization, stopword removal, and Sastrawi stemming. TF-IDF was used for feature extraction, with dataset splits of 60:40, 70:30, and 80:20. Naïve Bayes achieved its best accuracy of 73.47% at 70:30, while SVM achieved 89.07% at the same ratio. The results indicate that SVM provides better classification performance than Naïve Bayes in identifying user sentiment toward the SpeedCash application.
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