Author

Helena Margaretha

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Conference Sep 2026

Credit Default Prediction Using Logistic Regression and XGBoost: A Multi-Scenario Evaluation with SHAP-Based Interpretability Analysis

This study compares Logistic Regression (LR) and Extreme Gradient Boosting (XGB) for credit default prediction using 554,349 fintech loan records with a 7.95 percent default rate. Twelve scenarios covered 70-to-30 and 80-to-20 train-test splits, under both imbalance conditions, plus out-of-time validation from October...

Keiza Julita, J. Hefni, Helena Margaretha et al. · 2026 3rd International Confe... · 0 citations

Conference Sep 2026

Stock Price Prediction Using Moving Average Crossover with LSTM and Hybrid LSTM Models

Stock price fluctuations pose challenges for investors in making optimal decisions. This study evaluates the performance of LSTM with two input features and hybrid models combining LSTM with CNN, ANN, and RNN, enhanced by buy–sell–hold signals derived from the moving average crossover method. Hyperparameter tuning is a...

Helena Margaretha, Ryuki Ranawidjaja, Lina Cahyadi · 2026 3rd International Confe... · 0 citations

Conference Sep 2026

Forecasting Oil and Gold Prices with Regime and Structural Demand Features

This study investigates one-step-ahead forecasting of West Texas Intermediate (WTI) crude oil and gold prices over 2018–2025, with out-of-sample evaluation from 2022 to mid-2025. Linear Regression, XGBoost, and Random Forest are compared using feature sets that include cross-market signals, regime indicators, and globa...

Angelica Trifena Supriyanto, Shevane Evelyn Nadine Purnama, Helena Margaretha et al. · 2026 3rd International Confe... · 0 citations