Enhancing Question Classification in Educational Chatbots Using RASA Natural Language Understanding - Christanto, Zaenur Dwi; Hadiono, Kristophorus (Informatika, Universitas Jenderal Soedirman, 2026)
This research develops a chatbot model based on Rasa Framework to understand and respond to questions related to informatics learning, addressing the critical need for personalized AI-driven educational tools in Indonesian secondary education. The model is trained to recognize various patterns of student questions about informatics materials, especially the topic of number conversion. Using Natural Language Understanding (NLU), the chatbot model is developed to process natural language and classify the intent of student questions. Evaluation of the model using the confusion matrix showed good performance with 91.5% accuracy, 94.4% average precision, and 100% recall. The test results showed that the model was able to correctly classify various types of intent, where eight out of nine intents achieved a perfect precision of 100%, with one intent, tutorial_calculation_octal_to_decimal, having a precision of 50%. The 100% recall across all intents demonstrates the model's comprehensive ability to identify all cases requiring responses, ensuring no student queries are missed. This research significantly contributes to computer science education by validating RASA's effectiveness for domain-specific NLU in low-resource educational settings, providing a scalable foundation for AI-based learning assistance tools that can enhance digital literacy and computational thinking skills among junior high school students.