Projects & Case Studies

Throughout my academic and professional journey, I've actively participated in projects focused on developing forecasting models and conducting weather data analysis utilizing machine learning techniques, notably the hybrid ANN-ARIMA for predicting monsoonal patterns.Additionally, I contributed to community service initiatives by integrating the Weather Research and Forecasting (WRF) computational model to develop advanced flood risk assessment tools and designed user-centric web applications to distribute early warning alerts, enhancing proactive disaster mitigation strategies.

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Achievements & Publications

PUBLISHED: Propagation Characteristics of Madden Julian Oscillation in the Indonesian Maritime Continent: Case Studies for 2020-2022, Agromet Journal, doi: 10.29244/j.agromet.38.1.1-12PREPRINT / UNDER REVIEW: An Elementary Approach to Predicting Indonesian Monsoon Index: Combining Ann-Arima Hybrid Method and Practical Use

• ResearchGate

GitHub: Mock Projects

One of my enjoyable side projects I'm proud of is the YouTube video recommendation insights project. I utilized the YouTube Data API and NLP to analyze users' watch histories, uncovering insights into video recommendations. Key features include OAuth 2.0 integration, transcript analysis, keyword extraction, visualization, and data export capabilities.

Another one is on Customer Goods Data Modeling project that addresses industrial challenges through predictive modeling for daily sales quantity and customer segmentation. It utilizes PostgreSQL and DBeaver for data ingestion, Tableau Public for interactive dashboards, Python in Google Colab for predictive modeling, including time series ARIMA, and clustering techniques.

• Check out my codes

Professional Certifications

Coursework Certifications

WORK EXPERIENCE

• Data Analyst - LinkAja

• Model development:
- Implemented PCA and Isolation Forest for anomaly detection in merchant-customer transactions.
- Implemented Random Forest and Network Analysis to map illegal online gambling transactions.
- Developed semi-supervised Relational Graph Convolution Network (RGCN) model for collusion risk assessment.
- Developed a gradient boosting model that utilizes the (RGCN) model for collusion risk assessment.
• ML deployment: Utilized GCP cloud automation tools including Cloud Functions and Virtual Machine for model deployment.
• BAU: Utilized device intelligence to monitor tampering, cyberattacks, account takeovers, and computer vision for eKYC verification.

• Data Scientist - Kalbe Nutritionals

Utilized PostgreSQL in DBeaver for data exploration, developed interactive Tableau dashboards, employed ARIMA time-series regression in Python to estimate daily product quantities for inventory management, and applied K-Means marketing strategies and providing personalized promotions based on customer segmentations.

• AI Engineer / NLP Developer - Seikat AI Software House

Designed and deployed a Flask-based recommendation system API incorporating NLP techniques such as topic modeling, named entity recognition, and sentiment analysis, utilizing Docker for containerization and Kubernetes for scalable deployment, enhancing personalized job matching between seekers and listers.

• Previous Work Experiences

• Machine Learning Researcher - EST Research Group ITB
• Full-Stack Developer Internship - BTPN Syariah
• Front-End Developer Internship - Core Initiative Studio
• Data Science Research Internship - LAPAN/Indonesian National Aeronautics & Space Administration
• GIS Data Analyst Internship - Garda Caah

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