Awards & Achievements
Recognition from national and international AI/ML competitions, showcasing innovative solutions across healthcare, security, and sustainability
Finalist
Finalist - GEMASTIK 2025 Data Mining Competition
Ministry of Higher Education, Science, and Technology
Advanced to the final round among 200+ teams nationwide by developing a Graph-of-Graphs Neural Network (GoGNN) integrated with TabPFN for cooperative business classification, demonstrating expertise in advanced graph neural networks, hierarchical modeling, and large-scale data analysis.
Semifinalist
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
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
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
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
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.