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About Me

Hii there! I'm Fariz, an Informatics student based in Yogyakarta, Indonesia. 👋

Currently, I'm shifting my gears to become a Full-Stack Web Developer. I believe that building a great website isn't just about writing code it's about understanding the users (UI/UX) and the data behind it.

I love exploring different fields to strengthen my developer intuition:

  • Design Sense: Validated by winning the Website Development Competition at IOFEST 2025 (Universitas Tarumanagara).
  • Analytical Skill: Sharpened by reaching the Intermediate Level in Data Science at IDCamp 2023.

Now, I combine these skills to build web applications that are robust, data-driven, and easy to use.

  • Currently working on: Building end-to-end web solutions (Front-End & Back-End).
  • Learning in 2026: Advanced Backend Architecture, Database Optimization, & System Security.
  • Fun fact: I write code with a Data Scientist's logic and style it with an Award-Winning Designer's taste.

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Featured Projects


1. Platform TradeFariz

Role: Fullstack Web Developer & AI Integration

React Supabase TypeScript Groq AI

Asisten trading berbasis AI yang terintegrasi secara real-time dengan database Supabase. Menggunakan model Llama 3.3 (via Groq) untuk menganalisis riwayat transaksi, manajemen risiko, dan memberikan insight strategi trading yang dipersonalisasi bagi pengguna.


2. FRIZCODE: UMKM Digital Solutions

Role: Fullstack Web Developer

Next JS Tailwind Framer Motion Supabase

Sebuah platform gamified learning berbasis web yang dirancang untuk melatih logika pemrograman melalui studi kasus digitalisasi UMKM di Indonesia. Proyek ini menggabungkan pengalaman belajar interaktif dengan sistem manajemen agensi IT profesional.


3. Laundry Sepatu Fariz

Role: Front-End Web Developer

TypeScript React Tailwind

Platform layanan cuci sepatu digital. Pelanggan bisa Lacak Resi (Tracking) status cucian secara real-time, booking via WhatsApp, dan cek lokasi via Maps.


4. Al-Quran Sentiment Analysis

Role: Data Scientist

Python Scikit-Learn Pandas

Proyek Data Science End-to-End untuk klasifikasi ulasan aplikasi. Menggunakan algoritma Logistic Regression dengan Akurasi 93%.


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