Adam Gumilang
Full-Stack Developer & AI Engineer
Informatics Engineering student experienced in backend, web development, and basic AI, with a strong motivation to learn, collaborate remotely, and build real-world technology solutions.
About Me
I am a full-stack developer passionate about crafting digital experiences that live on the web. My focus is on building accessible, pixel-perfect, and performant applications that solve real-world problems.
Experience
2+ Years
Focus
Full-Stack Web
My Education
A journey of learning, exploring new technologies, and building a foundation in engineering.
Nusa Putra University
Faculty of Engineering, Computer and Design
"Education is the most powerful weapon which you can use to change the world."
Skills & Technologies
I leverage a modern tech stack to build robust, scalable, and intuitive applications. Always exploring new tools to enhance development and user experience.
Frontend
Backend
AI & Machine Learning
Tools & DevOps
Featured Projects
A selection of things I've built, ranging from web applications to open-source tools.
Ginvitations
Built a customizable digital wedding invitation platform designed to streamline the creation and management of online invitations. The project involved developing robust backend services to handle invitation data efficiently, ensuring reliability and scalability. In addition, a fully responsive user interface was implemented to provide an optimal experience for both mobile and desktop users.
KKN Kabandungan
Developed a web platform to introduce KKN activities and showcase the team profile, providing clear information and accessibility for users. The platform also included a career guidance feature designed to help students identify suitable career paths based on their interests and potential.
Banana Classification
Implemented a basic deep learning model to classify Cavendish bananas, focusing on accurate image-based prediction. The project also integrated the classification logic into a simple web-based interface, allowing users to interact with the model easily through an accessible online platform.
Waste Detection
Developed a real-time waste detection system using YOLOv8 trained on the TACO dataset, including data preprocessing, annotation management, and end-to-end training pipeline development in Python. The project involved training and evaluating object detection models while monitoring key metrics such as precision, recall, and mAP, as well as building a real-time inference workflow to detect and classify waste objects directly through a live camera feed.
