IndoBERT Model Analysis: Twitter Sentiments on Indonesia's 2024 Presidential Election - Putri, Dwi Ismiyana; Alfian, Ari Nurul; Putra, Mardi Yudhi; Mulyo, Putro Dwi (Journal of Applied Informatics and Computing, 2024)
Elections are one of the key moments in a country's democracy. Indonesian elections have a significant impact on regional and global politics. Twitter being one of the popular social media platforms becomes a powerful tool for political campaigns. This makes it an ideal source to analyze public opinion during the 2024 general election, particularly the upcoming Presidential Election (Pilpres). IndoBERT is the model chosen to analyse the sentiment from the dataset in this study using a zero-shot learning approach. Based on the evaluation results, the accuracy value of the 2024 presidential election classification is 0.60 (60%), tends to predict with a good value in the positive label of 0.74 (74%) for F1-Score. This model is considered quite good at predicting negative labels but the results are not too optimal with a value of 0.49 (49%). Confusion Matrix in this IndoBERT model is more likely to label tweets with positive things, by detecting negative labels quite well.