• Led and collaborated with another Android developer to maintain and improve the app.• Developed and managed a CI/CD pipeline for automated deployment to Play Store internal testers.• Deployed the app to the Google Play Store.• Organized and implemented new features.• Monitored branch merges to ensure code quality

• Developed the Android app implementation of Medicify, a mobile application for drug identification and information retrieval created to help visually impaired people.• Collaborated with cross-functional teams, including Machine Learning and Cloud Computing teams.• Deploying the Optical Character Recognition service from the ML Kit API, which is used to detect the text on the medication package.• Implement the API endpoint provided by the Cloud Computing team to retrieve the medication database and recommendations created by the Machine Learning team.

• Designed an entry-level Android Jetpack Compose curriculum for GDSC members.• Taught weekly live coding sessions for GDSC members, focusing on Jetpack Compose App Project.• Conducting collaboration with core teams from multiple divisions to achieve common goals by establishing effective communication and teamwork

• Selected as one of 500 students who took part in SIB Dicoding Batch 5, out of 7,377 registrants.• Selected as one of the top Capstone Project• Complete the Program and Graduate with Distinction title

• Selected as one of 5,010 students who took part in The Bangkit Academy, out of 67,000 registrants.• Selected as one of the top 20 projects out of 787 total projects to continue to Incubation Process.

Recoptic (Recognition Optic) - Student Creativity Program (PKM) 8 field• Developed a mobile-based application called Recoptic (Recognition Optic) to assist visually-impaired individuals.• Features include drug detection, text recognition, and object identification, all integrated with Talkback for an accessible user experience.• Successfully implemented ML Kit and TensorFlow libraries into the app, enabling it to reliably identify and retrieve data about medicine by scanning its packaging with a camera.• The app utilizes the ML Kit text recognition model to recognize the text on the package and provide users with comprehensive information about the medication, including its name, type, indication, usage guidelines, and dosage needs.