Comparative Evaluation of CNN-LSTM Model for Emotion Detection in Indonesian Text

This study developed an emotion detection model for Indonesian text using a dataset from prior research.The data was refined through multiple pre-processing steps before applying CNN-LSTM machine learning techniques.Comparative analysis indicated the model achieved 58% accuracy, lower than baseline methods.The results imply need for larger annotated corpora, improved text normalization, and integration with state-of-the-art deep learning approaches to enhance performance for Indonesian emotion detection.