Analisis Sentimen Ulasan Aplikasi Exchange Pintu Menggunakan Support Vector Machine Sentiment Analysis of Pintu Exchange Application Reviews Using Support Vector Machine

Pratama, Aldinoaufanda Yogi (2025) Analisis Sentimen Ulasan Aplikasi Exchange Pintu Menggunakan Support Vector Machine Sentiment Analysis of Pintu Exchange Application Reviews Using Support Vector Machine. Undergraduate thesis, Universitas 17 Agustus 1945.

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Abstract

The development of blockchain-based cryptocurrency technology has driven a significant transformation in the digital investment system in Indonesia. One popular local exchange platform is the Pintu app, which is trending, with its leverage trading feature, simple interface, and educational content for users. Some users have varying perceptions regarding slow transactions, high administrative fees, and frequent security issues. Therefore, an in-depth analysis of these reviews is necessary to understand user perceptions and improve the quality of the exchange app's services. This study aims to analyze user sentiment towards the Pintu app through reviews on the Google Play Store using the Support Vector Machine algorithm. Data was collected through scraping techniques from January 1, 2024, to January 1, 2025. The research stages included text preprocessing, sentiment labeling using a lexicon-based approach, and weighting using Term Frequency-Inverse Document Frequency. The classification process used the SVM method along with a confusion matrix evaluation, including accuracy, precision, and recall metrics. The best results were obtained in the 70:30 data split scenario with an accuracy of 92.42%. The SVM algorithm demonstrated superior performance compared to the Naïve Bayes method, particularly in classifying neutral reviews. The visualization results are displayed in the form of bar charts and word clouds for each sentiment category. This research is expected to contribute to the development of exchange application services and improve the user experience in the digital investment space.

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