Development of MyCare AI: A Dual-AI Mental Health Chatbot for Personalized Emotional Support - Arief, Zaenal Syamsyul; Hamzah, Muhamad; Azham, Moch Nazham Ismul (Aliansi Peneliti Informatika, 2025)
Access to mental health services remains a critical challenge in Indonesia, primarily due to societal stigma and limited availability of professional support. In response to this issue, this study introduces MyCare AI. This web-based mental health chatbot platform combines a Bi-LSTM-based emotion classification model with a generative conversational model provided by Google Vertex AI. This Dual-AI architecture enables the system to detect user emotions from Indonesian text inputs and deliver real-time, contextually appropriate, and empathetic responses. The emotion classification model is trained on a balanced English-language dataset representing four key emotional states: sadness, suicidal ideation, fear, and anger. The system employs a translation mechanism to convert Indonesian input into English before classification and then uses the detected emotion to condition the response generation process dynamically. The model achieved a classification accuracy of 95%, outperforming comparable models based on BERT-SVM and conventional LSTM architecture. This platform is intended for individuals who require immediate, anonymous, and continuous emotional support, including users in underserved or remote communities. MyCare AI represents a scalable and practical solution for digital emotional assistance and lays the groundwork for future integration with professional mental health services and native-language support frameworks. Key limitations include the system's reliance on real-time translation and an English-based dataset, highlighting the need for future development of culturally specific models.