Implementation of a Telegram-Based Child Consultation Chatbot Using IndoBERT
Abstract
Children’s health and development are crucial aspects that require proper attention from parents. However, many parents lack easy access to immediate consultation regarding their child's health and well-being. To address this issue, this study develops a child consultation chatbot on Telegram using the IndoBERT model. The chatbot utilizes data from Halodoc and Alodokter, structured into an intent-based format with 227 tags, 5,428 patterns, and 278 responses. The dataset undergoes preprocessing, including lowercasing, text cleaning, normalization, stopword removal, and stemming. Four preprocessing scenarios are tested, including the use of term frequency-based stopwords without applying stemming, the use of NLTK stopwords without stemming, the use of term frequency-based stopwords combined with stemming, and the use of NLTK stopwords combined with stemming. The best model, trained with an 80:20 training-validation split using term frequency-based stopwords without stemming, achieves 98% accuracy, 98.5% F1-score, 98.9% precision, and 98.5% recall. The chatbot successfully classifies user intent and ensures structured interactions through a confidence-based response mechanism. This research demonstrates that an IndoBERT-based chatbot can effectively assist parents in obtaining quick and relevant information regarding their children's health and development.
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