Virtual Booth

The increasing number of HIV cases among young adults, together with social stigma, limited awareness, and gaps in continuous health monitoring, highlights the need for accessible and privacy-conscious digital health solutions. This project presents MyHarapan, an intelligent mobile application designed to support HIV risk assessment, health monitoring, and awareness for both the general public and people living with HIV (PLHIV). The application adopts a dual-pathway approach, where general users are provided with exposure-based risk guidance, while PLHIV can monitor clinical indicators such as CD4 count, viral load, symptoms, and treatment adherence. An XGBoost-based prediction model is used to classify health risk, while additional modules provide targeted HIV education, community support, and location-based access to healthcare facilities. Due to privacy and availability limitations of real clinical data, a synthetic clinical dataset containing 2,000 records was developed for model training and evaluation. The implemented model achieved a 94.75% classification accuracy and 95.24% recall, with emphasis placed on reducing false-negative predictions for high-risk cases. Overall, MyHarapan demonstrates the potential of combining artificial intelligence, health literacy, and privacy-focused mobile technology to support proactive HIV self-monitoring and encourage timely access to appropriate healthcare services.