Analisis Sentimen Ulasan Aplikasi Bibit Menggunakan TF-IDF dan Support Vector Machine - Marselinus Dewadaru Bayu Adeodatus; Sepiyana Nopitasari; Wirda Arta Meivia; Hendrik Fery Herdiatmoko (CV. ITTC INDONESIA, 2026)
The rapid growth of financial technology (fintech) applications has increased the number of user reviews on digital platforms. These reviews contain valuable information regarding application quality, yet they are unstructured and difficult to analyze manually. This study aims to classify user review sentiments of the Bibit investment application into positive and negative categories using the Term Frequency–Inverse Document Frequency (TF-IDF) method and the Support Vector Machine (SVM) algorithm. The dataset was obtained from Kaggle, consisting of user reviews of the Bibit application collected from Google Play Store. The data were processed through several preprocessing stages, including cleaning, case folding, tokenization, stopword removal, and stemming. Feature extraction was performed using TF-IDF, and classification was conducted using SVM with a linear kernel. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The results indicate that the combination of TF-IDF and SVM provides good performance in classifying the sentiment of Bibit application user reviews.