Multi-Intent Chatbot for New Student Admission Information Using Artificial Neural Networks

Abstract

Digital transformation in higher education services requires the provision of New Student Admission information (PMB) services that are fast, accurate, and available on an ongoing basis. However, conventional PMB services still face the problem of limited service time, staff workload, and difficulty in handling user inquiries that vary and are multi-intent. This study aims to implement PMB information service chatbot based on Artificial Intelligence that is able to handle multi-intent questions effectively. The proposed approach integrates IndoBERT Pre-trained language model as semantic feature extractor with Artificial Neural Network (ANN) as intent classifier. The research Dataset consisted of 1,064 question variations grouped into 21 intent categories. The test results showed that the model achieved an accuracy rate of 96%, with the value of precision, recall, and F1-score of 0.96, respectively. The implementation of the system proves that the chatbot is able to provide relevant responses in both single-intent and multi-intent scenarios. Thus, this approach is effective in improving the quality and flexibility of chatbot-based PMB information services.