As a Fullstack Developer during an internship with the National Committee for Sharia Economics and Finance (KNEKS), contributed to the management and development of dashboards for the Sharia Economic Data Center. On the backend, utilized Express.js to build APIs, PostgreSQL for robust database management, and Docker for efficient containerization on the KOMDIGI VPS server. On the frontend, leveraged React.js for user interfaces, combined with Redux and Redis to ensure high-performance state management and responsiveness. Played a pivotal role in end-to-end integration, optimizing system performance, and ensuring data security while adhering to industry standards in the Islamic finance and economics sector.

As the Tech Lead for the Hibogi project under the Faculty of Dentistry, Jenderal Achmad Yani University, led the development of a cross-platform mobile application using React Native. This AI-driven teledentistry solution focuses on enhancing remote dental health diagnostics. Responsible for backend development with Express.js, managing data efficiently with SQL and Redis, and deploying the application using AWS for scalability and reliability. Strategically designed business workflows to improve operational efficiency and reduce costs, ensuring the application meets both technical and user needs.

In addition to the mobile version, spearheaded the development of the Hibogi web application. Designed the platform to provide seamless integration with the mobile app, allowing users to access teledentistry services via a browser. Leveraged modern web technologies to create a responsive, user-friendly interface while maintaining backend robustness with Express.js and efficient database solutions. Integrated AI features to assist users with remote dental health assessments, making teledentistry more accessible and effective.

Contributed as a Software Developer to the OrthoCVMS project under the Faculty of Dentistry at Padjadjaran University. The project focuses on developing innovative teledentistry software for analyzing skeletal maturity using a rule-based approach. Designed and implemented algorithms to automate Cervical Vertebrae Maturation (CVM) analysis in line with orthodontic medical standards. Ensured system accuracy, reliability, and efficiency to support faster and more effective clinical decision-making. This web-based solution enhances the accessibility of orthodontic diagnostics, streamlining the process for practitioners and patients alike.