Awards & Achievements

Recognition from national and international AI/ML competitions, showcasing innovative solutions across healthcare, security, and sustainability

Semifinalist - Health-themed AI Hackathon
International

Semifinalist

March 2025

Semifinalist - Health-themed AI Hackathon

National University of Singapore

Designed a two-stage EEG neurological events detection pipeline using EEG-DETR (EEG Detection Transformer), which incorporates Multimodal Convolutional Neural Network as the feature extractor for identifying neurological events in brain signal data. Competed against 200+ global teams.

Finalist - AI Hackathon
National

Finalist

Jan 2025

Finalist - AI Hackathon

Institut Teknologi Bandung and Telkomsel

Progressed past 330+ teams with TRACE: Traceable Real-time Adaptive Continual Explainable Deep Fake Detection System using Audio Visual Feature Fusion model, implemented with FastAPI web framework and PyTorch deep learning framework for large-scale video data processing.

1st Winner - Data Analysis Competition
Southeast Asia Regional

1st Winner

Oct 2024

1st Winner - Data Analysis Competition

Institut Teknologi Sepuluh Nopember

Placed first among 355+ teams across Southeast Asia by implementing data analysis and machine learning solutions, including AutoML modeling and feature engineering techniques for solar power output prediction.

Finalist - Dataquest Competition
National

Finalist

Sep 2024

Finalist - Dataquest Competition

Universitas Airlangga

Developed an Intrusion Detection System using Gradient-Boosted Tree Model for multiclass network traffic classification with iterative feature engineering based on data analysis. Reached finals among 200+ nationwide teams.

3rd Winner - RISTEK Datathon
International

3rd Winner

Aug 2024

3rd Winner - RISTEK Datathon

RISTEK Fasilkom UI

Placed 3rd among 300+ international teams by developing a Bank Loan Fraud Detection System using Temporal Graph Neural Networks in the qualification phase, then developed an e-commerce Semantic Search Engine using Cross-Encoder architecture and Contrastive Learning in the finals, improving product search relevance for better customer experience.