Sentiment Analysis On Indonesian Tweets about the 2024 Election - Sembiring, Alfan Ramadhan; Dewa, Chandra Kusuma (Sinkron : jurnal dan penelitian teknik informatika, 2025)
This study investigates public sentiment on Indonesian Twitter regarding the 2024 General Election, employing machine learning and deep learning techniques, including Naïve Bayes, Support Vector Machines (SVM), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The dataset was collected using a Tweet Harvest method with the keyword "Pemilu" and underwent preprocessing steps such as case folding, removal of symbols and URLs, stopword elimination, and tokenization to ensure data quality. Performance evaluation metrics, including accuracy, precision, recall, and F1-score, were applied to assess the models' effectiveness. Naïve Bayes achieved the highest accuracy of 64%, followed by SVM at 63%, LSTM at 60%, and GRU at 57%. The findings indicate that traditional models like Naïve Bayes and SVM perform effectively on smaller datasets with structured features, while deep learning models excel in capturing complex sequential dependencies. However, deep learning methods exhibited overfitting tendencies, indicating the need for better regularization and optimization techniques. Furthermore, it emphasizes the potential of integrating traditional algorithms with advanced methods to enhance sentiment classification accuracy and generalizability across diverse datasets.