Analisis Sentimen Ulasan Aplikasi Maxim Merchant dengan Support Vector Machine (SVM) dan Random Forest - Selly Rizkiyah; Indira Zein Rizqin; Milla Akbarany Baktiar Putri; Shindi Shella May Wara; Kartika Maulida Hindrayani (JDMIS: Journal of Data Mining and Information Systems, 2026)
The development of digital technology, especially mobile devices, has led to an increase in application-based services. One important aspect in app development is to deeply understand user perception and satisfaction. This study aims to analyze user sentiment towards the Maxim Merchant application based on reviews obtained from the Google Play Store platform. A total of more than 2800 Indonesian-language reviews were collected using web scraping techniques. The review data was processed through pre-processing stages such as text cleaning, normalization, tokenization, removal of unimportant words, and stemming. Sentiments are categorized into positive and negative based on the review score, where scores of 1 to 3 are considered negative, and scores of 4 and 5 are considered positive. Word cloud visualization is used to show the dominant words of each sentiment category. The data is then converted into numerical form using TF-IDF and selected using the Chi-Square method. Classification was performed using Support Vector Machine and Random Forest algorithms. The evaluation results show that the Support Vector Machine algorithm performs better in classifying sentiment, especially in handling high-dimensional text data.