A new study analyzed Indonesian student sentiment regarding AI adoption in higher education, comparing traditional machine learning with Transformer-based deep learning models. The research utilized a dataset of 2,295 labeled samples, including student opinions and lexical sentiment data. While Support Vector Machines (SVM) showed strong performance among machine learning approaches, the fine-tuned DistilBERT model achieved the highest accuracy and F1-score, demonstrating the superior ability of Transformer models to understand context. AI
IMPACT Demonstrates Transformer models' effectiveness in capturing context for sentiment analysis, offering a benchmark for similar educational AI adoption studies.
RANK_REASON Academic paper detailing model performance on a specific NLP task.
- arXiv
- DistilBERT
- LightGBM
- Machine Learning
- Random Forest
- Support Vector Machine
- TF-IDF
- Transformer-based Models
- Indonesian Higher Education
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