Lecturer & Researcher in Machine Learning, Recommender Systems, and Software Engineering
Politeknik Negeri Lhokseumawe | Alumni Universitas Gadjah Mada (UGM)

I am a lecturer and researcher focusing on the design, development, and evaluation of intelligent systems driven by machine learning and data mining techniques.

My research bridges:

• Recommender Systems
• Applied Machine Learning
• Data Mining & Pattern Discovery
• Intelligent Decision Support Systems
• Empirical Software Engineering

I am particularly interested in transforming real-world institutional and user behavior data into predictive and recommendation models with measurable performance improvements.

• Hybrid Recommender Systems
• Collaborative & Content-Based Filtering
• Machine Learning Model Optimization
• Educational Data Mining
• Intelligent Information Systems
• Experimental Evaluation & Model Benchmarking
• Scalable Software Architecture for AI Systems

Design and evaluation of hybrid recommendation techniques for educational and institutional environments.

Focus:

• Accuracy optimization (Precision, Recall, F1, RMSE)
• Cold-start mitigation strategies
• Model interpretability

End-to-end ML system development integrating data preprocessing, model training, evaluation, and deployment.

Focus:

• Feature engineering
• Model comparison studies
• Performance benchmarking

Utilizing data mining techniques to support strategic and operational decision-making in higher education.

Focus:

• Clustering & classification models
• Predictive analytics
• Behavioral pattern analysis

My workflow integrates:

1. Problem formalization
2. Data preprocessing & feature engineering
3. Model development & hyperparameter tuning
4. Experimental validation & benchmarking
5. Statistical evaluation
6. Reproducible research documentation

GitHub repositories are structured as experimental research environments, where datasets, models, and evaluation results are systematically version-controlled.

Machine Learning & Data Science

• Python
• Scikit-learn
• Pandas & NumPy
• Jupyter Notebook
• Model evaluation metrics & validation techniques

Software Engineering

• Laravel (Backend)
• RESTful API Design
• Database Modeling (MySQL)
• System Architecture & Modular Design

Research & Experimentation

• Comparative model analysis
• Cross-validation
• Statistical performance measurement

To develop scalable, data-driven intelligent systems that combine robust software engineering principles with rigorous machine learning experimentation, contributing to impactful scholarly publications and practical institutional solutions.

Open for:

• Research collaboration in recommender systems & ML
• Joint publications
• Supervised student research projects
• Applied intelligent system development

"Designing intelligent systems through data-driven experimentation and rigorous software engineering."