Professional Overview
Data Science student at Telkom University with hands-on experience in data analysis, data annotation, Python, SQL, Excel, and machine learning. Experienced in working with real-world datasets and developing practical AI/ML projects. Seeking opportunities in Data Science, Machine Learning, Data Analytics, and AI where I can contribute while continuo
Experience
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Built semantic job matching using multilingual-E5 embeddings.
- Applied Cross-Encoder re-ranking to improve matching relevance.
- Developed explainable recommendations using SHAP and templates.
- Integrated 460+ learning outcomes and 2,100+ job postings.
- Collaborated with researchers to evaluate recommendation algorithms.
Key Achievements / KPIs
- Integrated 460+ course learning outcomes across academic datasets.
- Implemented semantic matching and Cross-Encoder re-ranking.
- Developed explainable recommendations using SHAP-based analysis.
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Researched medical image segmentation using deep learning models.
- Implemented U-Net, U-Net++, Swin-UNet, and SegNet models.
- Preprocessed data and trained models for cervical cell segmentation.
- Evaluated models using Dice Score, IoU, and other relevant metrics.
- Analyzed results and collaborated with researchers to improve models.
Key Achievements / KPIs
- Developed and evaluated 4 deep learning architectures for segmentation.
- Compared U-Net, U-Net++, Swin-UNet, and SegNet performance.
- Conducted cervical cell segmentation research for manuscript preparation.
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Analyzed medical imaging datasets for model performance and data quality.
- Addressed class imbalance in medical imaging datasets.
- Compared CNN models using recall-sensitive evaluation metrics.
- Identified the best-performing architecture for imbalanced datasets.
- Presented findings to support technical and research decisions.
Key Achievements / KPIs
- Evaluated multiple CNN architectures for imbalanced medical datasets.
- Identified the best-performing model using recall-sensitive metrics.
- Supported model selection for recall-critical medical applications.
Education
Field of Study: Data Science
Areas of Expertise
AI & Machine Learning Engineers
Design, train, and ship production ML models — from data pipelines to model inference at scale. Covers supervised learning, neural networks, LLM integration, and model optimisation.
Data Science & Analytics
Extract business insights from raw data using statistical modelling, predictive analytics, A/B testing, and interactive dashboards.
NLP & Prompt Engineering
Build natural language processing systems, fine-tune large language models, and craft reliable production prompts for AI-powered products.
