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jasonwidjajadev/README.md

Hi there ๐Ÿ‘‹

Portfolio Resume LinkedIn

Iโ€™m a Computer Science student at the University of New South Wales, majoring in Artificial Intelligence, with hands-on experience building full-stack web applications, backend systems and data-driven ML projects.

I enjoy working close to the system level โ€” designing APIs, reasoning about data models, and turning ambiguous requirements into reliable, well-structured solutions.

Before transitioning into tech, I worked as a designer, which shaped how I approach engineering: thinking in constraints, iterating deliberately, and building things that are not just correct, but intuitive to use.

Thanks for stopping by โ€” feel free to explore my repos or reach out ๐Ÿ˜Š

๐Ÿ› ๏ธ Tech Stack

Python PyTorch NumPy Pandas Java C++ JavaScript Flask Node.js SQLite PostgreSQL MySQL React HTML5 CSS3 Linux Postman Git Vercel

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  1. basketwise basketwise Public

    A grocery comparison app that helps Australians find the cheapest practical basket across major supermarkets.

    Python 1 1

  2. sydney-bus-gtfs-api sydney-bus-gtfs-api Public

    Production-style REST API for real-world data ingestion, serving GTFS transit data with JWT authentication, role-based access control, stop search, route visualisation, and CSV exports.

    Python

  3. tributary-event-streaming-library tributary-event-streaming-library Public

    A lightweight Java event streaming library inspired by Apache Kafka, implementing topics, partitions, producers, and consumer groups.

    Java

  4. bittrickle-p2p-udp-tcp bittrickle-p2p-udp-tcp Public

    BitTrickle: Peer-to-Peer File Sharing Application

    Python

  5. agropest-object-detection agropest-object-detection Public

    Comparative computer vision study for agricultural pest detection using classical ML, Faster R-CNN, RetinaNet, and YOLO.

    Jupyter Notebook

  6. absa-cross-domain-generalization absa-cross-domain-generalization Public

    A study of cross-domain generalization in aspect-based sentiment analysis (ABSA), using the SemEval 2014 datasets and a custom 2026 dataset, focusing on aspect-term sentiment classification across โ€ฆ

    Jupyter Notebook