Optimasi Hiperparameter XGBoost Regression untuk Prediksi Harga Saham BBNI Berbasis Transaksi Historis
TL;DR
These findings confirm that the optimized model offers superior generalization capabilities in capturing price volatility compared to the baseline model and provides a robust analytical tool for investors and financial analysts to mitigate risks and formulate effective trading strategies in the highly fluctuating banking stock market.
Abstract
Stock investment in the banking sector, such as PT Bank Negara Indonesia (Persero) Tbk (BBNI), carries high risks due to dynamic market volatility, necessitating accurate prediction methods to support investment decision-making. This study aims to optimize the performance of the XGBoost Regression algorithm in predicting BBNI stock prices based on historical transaction data from the last five years. The methodology applied includes data preprocessing for price format validation, feature engineering using technical indicators (SMA, EMA, MACD, RSI), and hyperparameter optimization using the Grid Search Cross-Validation technique. The experimental results demonstrate that hyperparameter optimization effectively refines the model's predictive stability. While maintaining a highly precise Mean Absolute Percentage Error (MAPE) of 2.00%, the Grid Search technique successfully reduced the nominal error (RMSE) and improved the model's goodness-of-fit (R-Squared). These findings confirm that the optimized model offers superior generalization capabilities in capturing price volatility compared to the baseline model. Consequently, this optimized predictive model provides a robust analytical tool for investors and financial analysts to mitigate risks and formulate effective trading strategies in the highly fluctuating banking stock market.