Comparative Analysis of IndoBERT and LSTM for Multi-Label Text Classification of Indonesian Motivation Letter

Abstrak

The evaluation of motivation letters is a crucial step in the student admission process for one of vocational institutions in Indonesia. However, the current manual assessment method is prone to subjectivity and inconsistency, making it less reliable for fair student selection. This research presents a comparative analysis of two deep learning models, IndoBERT and Long Short-Term Memory (LSTM), for multi-label text classification of motivation letters written in Indonesian. Using a dataset of 676 motivation letters labeled with nine predefined categories, we evaluate the models based on their classification performance. The results indicate that IndoBERT outperforms LSTM, achieving an F1-score of 81%, compared to 76% for LSTM. This research provides insights into the effectiveness of IndoBERT for multi-label classification tasks in the Indonesian language and serves as a benchmark for future research in automating motivation letter evaluations.