Mental health issues in Indonesia are a growing concern, with alarming statistics from the I-NAMHS 2022 showing that 15.5 million adolescents, or 1 in 3, experience mental health disorders. The situation is exacerbated by unequal access to mental health services across different regions, with DKI Jakarta, Aceh, and West Sumatra recording the highest cases. Factors such as bullying, family conflicts, and traumatic experiences further increase the vulnerability of individuals, while early detection and intervention remain limited, especially for marginalized communities.
Mentalys aims to address this pressing issue by offering an innovative, technology-driven solution. Our mobile application leverages cutting-edge tools in machine learning, mobile development, and cloud computing to provide accessible, efficient, and inclusive mental health support.
- Identified critical mental health challenges in Indonesia:
- 1 in 3 adolescents (15.5 million) experiencing mental health disorders
- Unequal access to mental health services across regions
- Limited early detection and intervention mechanisms
- Conducted comprehensive research on mental health technology solutions
- Identified key pain points in existing mental health support systems
- Brainstormed innovative technological interventions
- Developed a comprehensive mobile application (Mentalys) addressing multiple mental health support needs
- Identified key technological components:
- Machine Learning for diagnosis
- Mobile development for user experience
- Cloud computing for scalable infrastructure
- AI-powered Personalization
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Machine Learning Models
- Sourced diverse datasets from Kaggle
- Developed three specialized models:
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Questionnaire Analysis (99% accuracy)
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Voice Emotion Detection (94% accuracy)
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Handwriting Analysis (84% accuracy)
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Mobile Development
- Used Kotlin and Android Studio
- Designed intuitive UI/UX
- Implemented key features:
- User authentication
- Mental health diagnosis
- Mood tracking
- Emergency support
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Cloud Infrastructure
- Utilized Google Cloud Platform
- Implemented secure authentication
- Created scalable backend services
- Integrated APIs for seamless functionality
| Name | Student ID | Learning Path | Contribution/Task |
|---|---|---|---|
| I Gede Widnyana | M014B4KY1819 | Machine Learning | I built the questionnaire model with an accuracy of 99% to support the mental health diagnosis feature. I handled the administrative aspects, managed the core idea of the Mentalys project, and guided the team to ensure the successful development and integration of key features. |
| I Nyoman Adi Mahendra Putra | M014B4KY1864 | Machine Learning | I have worked on a machine learning model for audio that can detect a person's emotions. Based on these emotions, it can classify whether someone is experiencing depression or not, aligning with the mental health topic we are addressing. The model currently achieves 94% accuracy. |
| Made Pranajaya Dibyacita | M014B4KY2373 | Machine Learning | I developed an image classification system using Convolutional Neural Networks (CNN) to analyze handwriting patterns for mental health disorder. The model processes handwriting samples as images and classifies them into two categories: those indicating no mental health concerns and those suggesting potential mental disorders. Through this deep learning approach, the model achieved an accuracy rate of 84% in detecting handwriting patterns associated with mental health conditions. |
| I Made Agus Budiarta | A403B4KY1851 | Mobile Development | I have developed a mental health test feature that includes a voice test, handwriting test, and questionnaire. Additionally, I implemented mental test history, motivational messages, definitions of mental states along with related articles based on test results,I also developed nearby clinic information using GPS. On the settings page, I included an "About Us" section containing terms of service, privacy policy, and app information. On the education page, I created a dialog displaying details about food content, view all food page, and view all articles page |
| Rezky Aditia Fauzan | A810B4KY3794 | Mobile Development | I created article pages. I created authentication screen. I created dark mode. I created multi-language translation. I created consultation screen. I create the infrastructure of the app. I managed the code. I did the finishing of the code and ui. I made sure the app runs smoothly. |
| Abdi Setiawan | C179B4KY0013 | Cloud Computing | I have created a complete REST API that includes Articles and Food API, Nearby Clinic API, and documentation using Swagger that can be accessed directly through a public URL. This REST API also includes dummy psychiatrist data integrated with Midtrans for payment, user authentication using Firestore, and data storage in Firebase. In addition, I integrated three machine learning models that have been deployed to cloud computing to support AI features, ensuring reliable performance. All APIs are deployed, ready to use with a seamless payment experience. |
| Shevabey Rahman | C179B4KY4138 | Cloud Computing | I collaborated with the team in developing the cloud infrastructure and integrating the backend and ML models. I successfully created a REST API for psychiatrist contacts with security implementation, and documented the API using postman that can be accessed via a public URL. In addition, I was involved in updating the food recommendation article data prepared by the Machine Learning team and model adjustments, as well as managing data storage in Firebase and images in Google Storage to support the needs of the Mobile Development team. I also monitored system performance and performed troubleshooting to ensure system stability and support the implemented AI features and models. |
- Android smartphone (Version 8.0 or higher)
- Minimum 200MB free storage space
- Active internet connection
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Download APK
- Visit: Mentalys APK
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Enable Unknown Sources
- Go to Settings > Security
- Allow installation from unknown sources
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Install the App
- Locate downloaded APK
- Follow installation prompts
- Grant necessary permissions
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First-Time Setup
- Complete onboarding
- Create user account
- Set language preference
- Ensure stable internet connection
- Check device compatibility
- Restart device if installation fails
| Component | Repository Link |
|---|---|
| Machine Learning | ML Audio, ML Handwriting, ML Quiz |
| Mobile Development | Mobile Repository |
| Cloud Computing | Rest API, API Nearest Clinic, API Article and Food Library |
This project is licensed under the MIT License - see the LICENSE file for details.
