Pengembangan Chatbot Kesehatan Mental Berbasis Web Menggunakan Model Long Short-Term Memory (LSTM)
- Akbar Ilham Ardin Universitas Dian Nuswantoro, Semarang, Indonesia
-
Abu Salam
*
Universitas Dian Nuswantoro, Semarang,
Indonesia
(*) Corresponding Author
Abstract
Mental health issues such as stress, anxiety, and academic burnout are increasingly prevalent among university students. However, many students remain reluctant or unable to access counseling services due to time limitations, social stigma, and a lack of available professionals. This study aims to develop CuraBot, a web-based chatbot designed to provide preliminary emotional support and mental health education in an instant, anonymous, and easily accessible manner for students. The system was developed using the Long Short-Term Memory (LSTM) algorithm, which is proven to be effective in understanding contextual text-based conversations. The dataset used consists of 1,624 conversational entries across 77 intent classes, adapted and localized from an open-source corpus to reflect the linguistic style and needs of Indonesian students. The development process involved several stages, including data preprocessing (lemmatization, tokenization, stopword removal, and padding), model training using TensorFlow, and deployment into a Flask-based web application. The model was evaluated using a separate test set of 244 entries, resulting in an accuracy of 89.9%, precision of 90.4%, recall of 89.1%, and an F1-score of 89.8%. These results indicate that the model can classify user intent with high accuracy. This research contributes to the development of a contextual, practical, and AI-based digital solution that supports early access to psychological services within university environments.
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