Nida Rifda Chairul

Implemented and evaluated LSTM, CNN-LSTM, and Transformer models to predict Nvidia stock trends using multi-series market signals. Engineered datasets with a 60×35 input shape, integrating technical indicators and correlated market data. Applied statistical forecasting and model tuning using RMSE, MAE, and loss curve analysis to identify the optimal architecture.

Implemented and assessed LSTM, CNN-LSTM, and Transformer models to predict NVIDIA stock trends using multi-series market signals from affiliated companies. Engineered datasets with a 60×35 input shape, integrating technical indicators and correlated market data. Applied statistical forecasting and model tuning using RMSE, MAE, and loss curve analysis to identify the optimal architecture.

Utilized SQL & Python to analyze consumer behavior, improving approval rates and risk assessment processes. Created interactive Tableau dashboards for a Kaggle-based project, visualizing key trends and strengthening data storytelling. Conducted fraud detection analysis on credit card data, identifying high-risk patterns and informing mitigation strategies. Identified behavioral risk indicators through advanced analytics, formulating strategies aligned with banking best practices.