
Data Mining Competition UNITY#14
YOGYAKARTA STATE UNIVERSITY • 2026
Developed a Siamese self-supervised learning model for post-disaster damage segmentation, achieving a 6% average improvement over the xBD paper baseline.

Backend Developer & Data Engineer
Artificial Intelligence Engineering (B.Sc.)
Physics, Chemistry, Informatics, Advanced Mathematics
Research and Technology Department Apprentice
Data Science and Artificial Intelligence Division
Data Science Mentor
Data Science Competition Staff
Volunteer, Elite TC Champions
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Contributions in the last year, from the GitHub profile graph

An efficient hybrid sequence modeling system for Indonesian speech synthesis using multilingual knowledge transfer.

A ResNet-based road damage classification system combined with a Graph Neural Network to prioritize efficient road maintenance.

A Siamese self-supervised learning model for segmenting damaged areas after disasters, evaluated against the xBD paper baseline.

A low-latency Retrieval-Augmented Generation pipeline integrated into a mobile application to support customer-worker peer-to-peer interactions.

Unsupervised clustering of 15 Indonesian local dishes with DINO features, UMAP, and K-Means: silhouette 0.6714, Davies-Bouldin 0.4707, trustworthiness 0.9639.
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