Telecommunication Engineering student building toward AI and machine-learning engineering. I work across model experimentation, inference services, and the web or mobile interfaces that make those systems usable.
- Applied computer-vision and machine-learning projects with TensorFlow, Keras, and scikit-learn.
- Python services with FastAPI, Redis/RQ, Flask, Supabase, PostgreSQL, and MongoDB.
- Web and mobile interfaces with Next.js, React, TypeScript, Flutter, and Dart.
- Reproducible project documentation that separates implemented behavior, evaluation evidence, and limitations.
| Project | What it demonstrates | Context |
|---|---|---|
| SCOVIS Frontend + Backend | Human-in-the-loop handwritten-answer score classification with Next.js, Supabase, FastAPI, Redis/RQ, and TensorFlow | Undergraduate thesis project; live web app |
| DermaScan | Multi-task skin-lesion decision-support prototype using TFLite, FastAPI, and React | Team capstone fork; my documented work covers model conversion, backend integration, and cloud deployment |
| QuizInt | Role-based learning prototype with quizzes, leaderboards, QR onboarding, PDF export, and Supabase | Academic team project built with Flutter and Dart |
| Vehicle Image Classification | Four-class transfer-learning workflow with a frozen MobileNetV2 backbone and TFLite/TF.js exports | Machine-learning project with notebook evaluation evidence |
| Bitcoin Forecasting | 24-step time-series forecasting with baseline LSTM, attention, and Seq2Seq experiments | Applied deep-learning notebook project |
| Gojek Sentiment Analysis | Indonesian review classification comparing Logistic Regression, SVM, and a dense neural network | NLP notebook project |
- AI and data: Python, TensorFlow, Keras, scikit-learn, NumPy, pandas, Jupyter, Pillow, OpenCV
- Backend and data services: FastAPI, Redis/RQ, Supabase, PostgreSQL, Flask, MongoDB
- Web and mobile: Next.js, React, TypeScript, Tailwind CSS, Flutter, Dart
- Deployment and tooling: Docker Compose, Caddy, Vercel, Railway, GitHub Actions
I am currently strengthening model evaluation, reproducible ML workflows, and reliable deployment patterns for applied AI systems. SCOVIS is the main end-to-end project connecting those interests.
- LinkedIn: linkedin.com/in/reyhadri
- Email: raihanhadriansyah111@gmail.com