Analisis Sentimen Menggunakan Metode IndoBERT Pada Ulasan Aplikasi Zoom Menggunakan Fitur Ekstrasi GloVe - Andhika, Firza Rafiandi; Witanti, Wina; Nurul Sabrina, Puspita (METIK JURNAL (AKREDITASI SINTA 3), 2025)
This research aims to conduct a sentiment analysis on Zoom application reviews by testing whether combining features from IndoBERT and GloVe can improve classification accuracy. The methodology begins with the collection of 5000 reviews from the Google Play Store, which then undergo a pre-processing stage. To address class imbalance, the Random Oversampling technique was applied. Features are extracted using IndoBERT for contextual meaning and GloVe for global semantic meaning, and their vectors are then combined through concatenation. The balanced dataset is divided into 80% training data and 20% testing data to train and evaluate the model. The test results show that the combined model achieved an overall accuracy of 91%, with a high precision value for the positive class (0.97) and a high recall value for the neutral class (0.95). For comparison, a model using only IndoBERT achieved 90% accuracy. Based on these results, it can be concluded that the combination of IndoBERT and GloVe is an effective and reliable approach for sentiment analysis, with its advantage lying in a richer feature representation due to the integration of global and contextual semantic information.