Designing a Library Chatbot Using Artificial Neural Network Methods to Improve Visitor Services

Abstract

Academic libraries increasingly struggle to provide consistent information services as digital collections grow and user inquiries become more varied. Staff-dependent communication channels such as email and live chat are constrained by working hours, leaving visitors without support during evenings, weekends, and holidays. This study proposes an intelligent text-based chatbot to automate visitor inquiry handling in a university digital library setting. The system employs an Artificial Neural Network (ANN) with a multi-layer Perceptron architecture trained on a corpus of 2,319 labeled samples across 56 intent categories. An Indonesian-language preprocessing pipeline was implemented, consisting of case folding, tokenization, stopword removal, and morphological stemming using the Sastrawi algorithm, followed by Bag-of-Words feature extraction producing 1,494-dimensional vectors. The system was deployed using a decoupled architecture, separating the Python Flask inference backend from a PHP CodeIgniter 4 frontend via an Ngrok HTTPS tunnel. Using an 80:20 stratified train-test split, the model achieved a classification accuracy of 91.00%, with a macro-average precision of 0.90 and recall of 0.92 on 464 test samples. Black-box functional testing confirmed stable real-time performance with response latency under three seconds. These results demonstrate that ANN-based intent classification can effectively reduce reliance on manual library staff for routine inquiries while providing continuous 24/7 automated support.