Implementation of a Bi-LSTM Model for Automatic Text Classification of Mathematics, Science, and Indonesian Language Questions

Abstract

This study aims to implement the Bidirectional Long Short-Term Memory (Bi-LSTM) model for automatic text classification of Mathematics, Natural Sciences (IPA), and Indonesian language questions to support efficient question grouping in digital education systems. The dataset used consists of 2,718 questions, which are evenly distributed across three subject categories. The research stages include text preprocessing, tokenization and padding, splitting the dataset into training and testing sets, designing the Bi-LSTM model architecture, and conducting training and evaluation using accuracy, precision, recall, and F1-score metrics. The results show that the Bi-LSTM model achieves an accuracy of 97% on the test data, with an average F1-score of 0.97. The confusion matrix analysis indicates that most predictions are correctly classified with a relatively low misclassification rate across categories. Based on these results, it can be concluded that the Bi-LSTM model is effective for automatic text classification of educational questions and has strong potential for further development in technology-based question grouping systems.