PREDIKSI EMOSI DALAM TEKS BAHASA INDONESIA MENGGUNAKAN MODEL INDOBERT - Saputra, Ade Chandra; Saragih, Agus Sehatman; Ronaldo, Deddy (Universitas Palangka Raya, 2025)
This study aims to predict emotions in Indonesian text using the IndoBERT model. Emotions play an essential role in human communication and have a significant impact on sentiment analysis and natural language processing. In Indonesia, the lack of optimized datasets and models for emotion analysis in the Indonesian language poses a major challenge. This research utilizes IndoBERT, a BERT-based model specifically trained for Indonesian, to predict six categories of emotions: anger, sadness, happiness, love, fear, and disgust. The research methodology includes data collection from social media X, data preprocessing, emotion labeling, model training, and performance evaluation using metrics such as accuracy, precision, recall, and F1-score. Results show an overall model accuracy of 73%, with strong performance in recognizing emotions like