Computer Science @ Universitas Indonesia • Passionate in AI & Data Science • AI Competitions Enthusiast
I'm a Computer Science student at Universitas Indonesia who’s deeply passionate about Artificial Intelligence, Data Science, and Large Language Models (LLMs). I've had hands-on experience in real-world machine learning projects—from natural language processing to computer vision—and I'm always hungry to learn more.
Currently serving as Vice Director of Data Science & AI at RISTEK Fasilkom UI, where I help shape cutting-edge AI education for fellow students.
I'm looking for DS&AI internship opportunities to expand my experience and contribute to impactful machine learning systems.
Graph-Based Predictive AI for Hereditary Disease Risk Assessment
Heredicheck-AI is a full-stack AI system using Graph Neural Networks (GNN) to predict hereditary disease risks by modeling family relationships from synthetic medical data. Built as part of the MeldRx Predictive AI App Challenge, it features:
- FHIR-compliant synthetic patient generation using Synthea
- Graph construction with bi-directional family relationships
- Multi-label prediction of 6 hereditary diseases
- GNN modeling using PyTorch Geometric
- FastAPI backend and Vercel frontend deployment
Technologies used: Python, PyG, FastAPI, Scikit-learn, TF-IDF, GCNConv, Synthea
A research-driven classification system for JAKI (Jakarta Smart City) citizen reports using IndoBERTweet with Continual Learning.
- Trained on 150k+ real reports from 2021–2024
- Achieved F1-Micro Score 0.7465
- Targets government agency classification from textual complaints
- Tech stack evolving from Django + FastAPI to Next.js + Django REST Framework
- Submitted for GEMASTIK 2024
- Email: fassabilf@gmail.com
- LinkedIn: linkedin.com/in/fassabilf
Just keep swimming, just keep swimming.