Development of MyCare AI: A Dual-AI Mental Health Chatbot for Personalized Emotional Support
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| Title: | Development of MyCare AI: A Dual-AI Mental Health Chatbot for Personalized Emotional Support |
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| Authors: | Zaenal Syamsyul Arief, Muhamad Hamzah, Moch Nazham Ismul Azham |
| Source: | Journal of Intelligent Systems Technology and Informatics. 1:45-52 |
| Publisher Information: | 2025. |
| Publication Year: | 2025 |
| Description: | 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. |
| Document Type: | Article |
| ISSN: | 3109-757X |
| DOI: | 10.64878/jistics.v1i2.34 |
| Rights: | CC BY |
| Accession Number: | edsair.doi...........2bcc2071719c11c322555bd2b504e12a |
| Database: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstract: | 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. |
|---|---|
| ISSN: | 3109757X |
| DOI: | 10.64878/jistics.v1i2.34 |