SobatKamera is a full-stack camera rental marketplace I designed and built solo, where photographers can rent gear from nearby stores and sellers can run their own shop. Behind the simple browsing experience is a backend built for correctness: row-level locks, conditional stock updates, and optimistic locking keep two customers from ever booking the same camera, and parallel-request tests verify it. Payments run through Xendit with verified webhooks and safeguards that protect paid orders. The catalog is sorted by distance and rental radius is enforced server-side, and a dedicated BullMQ worker handles background jobs while the API stays up even if Redis goes down.

Worked as backend of an app that helps people find out whether a message or link they received is a scam. Users submit a chat or SMS message, or a URL, and the API returns a verdict of safe, suspicious, or scam with a confidence score. I built it with FastAPI and PostgreSQL and integrated two Machine Learning models into it: a Naive Bayes + TF-IDF model that classifies messages as normal, promotional, or fraudulent, and a Random Forest model that scores URLs on structural features such as length, IP usage, HTTPS, and suspicious symbols. The API also has a complete authentication flow: email OTP verification on sign-up, JWT access tokens paired with httpOnly-cookie refresh tokens, Argon2 password hashing, and password reset. Registered users can view their detection history with pagination and manage it, while visitors without an account get three free detections per day, tracked by a hashed IP address. The codebase is organized into domain-based modules with a clear router–service–model separation, uses Alembic for database migrations, and is covered by 51 automated tests written with pytest.

Spectra is an augmented reality (AR) learning application designed to help children with Autism Spectrum Disorder (ASD) practice object recognition in an interactive, low-friction way. The app overlays 3D learning objects onto the real-world environment through the device's camera, allowing children to explore and engage with educational content in a more visual and hands-on manner than traditional learning methods. The project was developed as part of an undergraduate thesis, with a strong focus on improving the stability and reliability of AR object tracking so that the learning experience stays smooth and distraction-free for young users.

Worked as a Fullstack to develop web app that shows current weather conditions and a 24-hour forecast for any city in the world, built as my capstone project for the IBM SkillsBuild program. I built it with Next.js, React, and TypeScript, and it gets live data from the WeatherAPI service using Axios. Users can search for any city or tap one of the quick-access buttons for popular locations such as Jakarta, Bandung, and Tokyo. The app then shows the current temperature, the “feels like” temperature, and the sky conditions, followed by an hourly forecast with temperatures, weather icons, and the chance of rain in a horizontally scrollable strip. Each search request is debounced, and the app handles errors clearly, showing a specific message when a city can’t be found and a separate one for network problems. The interface is responsive, styled with Tailwind CSS, and uses a glassmorphism look over an animated gradient background. The code is split into reusable components with typed props and API data.

CineView is a mobile application developed to allow users to review and rate movies they have watched. This project was created in response to the growing interest in digital movie-watching and opinion-sharing activities. The application is supported by a RESTful API built with Node.js and Express.js, with MongoDB used for data storage. The backend provides services for user registration and login, as well as handling and managing application data through a structured architecture consisting of controllers, models, routes, and middleware. The API is deployed using Vercel to provide backend services for the CineView Android application.

A Next.js–based web platform for multimedia competitions that provides a participant registration system with OTP validation, competition submission management with integration for GitHub/YouTube/demo links, a participant dashboard to monitor verification status and progress, a comprehensive gamification system with points, leaderboards, and reward redemption, an admin panel to manage participants, submissions, judges, products, and gamification, as well as a judge panel for reviewing and scoring submissions with feedback—all designed to be responsive across various devices.