NLP-Based Intent Classification Model for Academic Curriculum Chatbots in Universities Study Programs

NLP-Based Intent Classification Model for Academic Curriculum Chatbots in Universities Study Programs

Chatbot, IndoBERT, Intent Classification

Chatbots are increasingly prevalent in various fields, including academic fields. Universities often rely on lecturers and staff for information access, which can lead to delays, limited availability outside working hours, and the risk of missedquestions. This study aims to develop a chatbot model capable of addressingquestionsabout the curriculum through intent classification, reducing reliance on manual responses, and providing a solution that ensures quick,accurate information retrieval. The research focuses on optimizingthe IndoBERT model for intent classification andaddresses challengesthatarose due to imbalanceddata, which could have impacted model performance. Data was collected through an open poll on common curriculum-related questions asked by students.To address data imbalance, wetried oversampling techniques, such as SMOTE, B-SMOTE, ADASYN, and Data Augmentation. Data augmentation was chosen and successfully addressed the imbalance problem while maintaining data semantics effectively. Weachieved the best model with hyperparameters batch size of 8,learning rate of 0.00001, 15 epochs, and 64 neurons in the hidden layer,resultingin 98.7% accuracy on the test data. Evaluation metrics further demonstrate the model's robustness across multiple intents. This research demonstrates the advantages of the IndoBERT model inintent classification foracademic chatbots, achieving excellent performance

Najma Rafifah Putri Syallya1*, Anindya Apriliyanti Pravitasari2, Afrida Helen

https://jurnal.iaii.or.id/index.php/RESTI/article/view/6276/1017

Department of Statistics, Universitas Padjadjaran, Bandung, Indonesia

14-02-2024

FAJAR BAGUS W

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