IT professional with 3 years of experience in managing end-user IT support systems and leading projects in data analytics and data science. Skilled in hardware and software troubleshooting, IT asset and storage management, and IT end-user system oversight. Experienced in building data pipelines and implementing best practices in data security and integrity.
- Languages & Tools: Python, SQL
- Visualization: Tableau, Kibana
- Frameworks & Platforms: Streamlit, Docker, Apache Airflow, Great Expectations
Objective: Analyze property price trends to guide investment strategy
Role: Data Engineer
Highlights:
- Designed and implemented DAGs using Apache Airflow
- Configured containerized environments with Docker for Airflow, PostgreSQL, and Kibana
- Built dashboards in Kibana to visualize price trends
- Implemented data validation using Great Expectations to ensure integrity
Tools: Python, Docker, PostgreSQL, Kibana, Airflow
Overview
AISeeYou is an AI-powered application designed to detect suspicious objects from X-ray scans quickly and accurately. It aims to enhance security screening by automating the identification of hazardous items such as knives, scissors, and Swiss Army knives using computer vision models.
Problem
Manual interpretation of X-ray images is time-consuming and prone to human error. AISeeYou addresses this by leveraging object detection models to improve accuracy and speed in security inspections at airports, buildings, and public venues.
Solution Highlights
- Trained YOLOv8 and Faster R-CNN models on Kaggle X-ray baggage dataset
- Visualized label distribution (Swiss Army Knife = 23.1%, Unidentified Object = 22.5%)
- Deployed real-time detection app with Streamlit on Hugging Face Spaces
- Achieved >80% mAP@0.5 using Ultralytics evaluation tools
Technologies
Python, YOLOv8, OpenCV, Streamlit, TensorFlow, Hugging Face
Future Work
Enhancing dataset diversity, applying data augmentation, model fine-tuning, and exploring other architectures like EfficientDet.
Objective: Analyze helpdesk ticket patterns to uncover delay causes
Role: Data Analyst
Highlights:
- Performed descriptive and inferential statistics on ticket resolution times
- Visualized issue status distributions and root causes of pending tickets
- Delivered insights to improve operational efficiency
Tools: Python, SQL, Tableau
Objective: Predict car prices using supervised machine learning
Role: Data Scientist
Highlights:
- Conducted EDA, handled missing values & outliers
- Engineered relevant features
- Built and evaluated multiple regression models
- Deployed model using Hugging Face for inference demo
Tools: Python, Streamlit, Scikit-learn
- Email: [angga.fpriyanto@gmail.com]
- GitHub: github.com/angga7353
- LinkedIn: linkedin.com/in/angga fadhlurrahman prianto