Rafika Damayanti SN

I’m a Data Science lecturer and statistician with a Master’s degree in Statistics and Data Science. I publish quantitative research and bring international research experience to every model and teaching task.

I apply machine-learning and statistical methods to evaluate complex analytical problems with care—especially Random Forest-based propensity score modelling, cross-validation, hyperparameter optimization, and imbalanced-data considerations. I focus on methodological correctness, defensible reference solutions, and clear interpretation of what the results do (and don’t) support.

In my teaching role, I design and assess statistics/data-science problems that require structured reasoning, mathematical justification, code-supported analysis, and interpretation. I review student Python/R work to identify syntax errors, unsuitable methods, assumption violations, unsupported claims, and risks like overfitting and data leakage—then I guide revisions with precise written feedback.

My research work strengthens my practical approach: using SWAT and ArcGIS for water-quality and environmental analysis, and translating quantitative evidence into clear recommendations. I also build reproducible R reports and technical narratives that communicate uncertainty, limitations, and performance evaluation in a way my audience can trust.