~/portfolio $ profile
ML/AI · Data Scientist · Published IEEE Researcher
I am a published IEEE researcher and Computer Science student at Binus University. I have hands on experience in machine learning, data mining, NLP, and computer vision, building ML pipelines that turn unstructured data into actionable insights.
I worked on projects that span handwriting recognition, professional profile classification, and IoT hardware development, contributing across the full lifecycle from ideation to deployment and publication.
[languages]
[ml_ai]
[data_analytics]
[frameworks_tools]
[design_media]
NLP · Machine Learning
Reads LinkedIn profile text and predicts whether someone holds a high-level leadership role, using DistilBERT and a trained classifier to return a confidence score.
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Data Analysis · Visualization
Compares Indonesian and global film-genre consumption on streaming, built on FlixPatrol data across six periods with custom candlestick charts and trend lines.
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Computer Vision · Deep Learning
Upload a photo of handwritten notes and get clean, structured Markdown back, powered by a YOLO layout model and a custom character-recognition model.
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Front-End · Web App
A browser-based collage editor for gallery-style image layouts, entirely client-side. Arrange photos across grid presets and export the result as a JPG.
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UI/UX Design · Figma
A travel companion app concept for discovering and planning trips around Indonesia. A high-fidelity UI/UX design and interactive prototype built in Figma.
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Computer Vision · Deep Learning
Analyzes an uploaded photo and predicts the type of landscape it shows. A custom-trained CNN returns the top-3 predictions with confidence scores.
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Mobile · Full-Stack
A mobile budgeting app built with Flutter to log expenses, monitor balance, and control personal finances, backed by a JWT-secured REST API.
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Analyzing Key LinkedIn Profile Factors for Securing Employment with Leading Roles
2025 6th International Conference on Artificial Intelligence and Data Sciences (AiDAS)
This research that we have conducted analyzes which LinkedIn profile factors are most associated with securing high-level employment roles. Utilizing machine learning models and SHAP explainability, we found that recommendations, interests, experiences, and connections were more influential than popularity-based metrics such as follower count. The findings provide practical insights for professionals seeking to optimize their LinkedIn profiles and improve their career visibility.
[link]Bachelor's of Computer Science
Bina Nusantara University — GPA 3.77 / 4.00
Sep 2023 — Present
Exchange Student
Hanyang University, South Korea
Mar 2026 — Jun 2026
Technical Officer
BioCard
Sep 2025 — Jan 2026
Coordinator of Creative Media
B-Preneur (Binus Entrepreneur)
Mar 2024 — Feb 2026