Abstrak
Gold price forecasting is challenging in the financial sector due to its volatile and nonlinear movements, which are influenced by various economic factors. This study aims to implement and optimize a Bidirectional Long Short-Term Memory (BiLSTM) model for forecasting weekly gold prices. The data used consist of weekly world gold closing prices from 2020 to 2024 obtained from Yahoo Finance. The data preprocessing stages include normalization using Min-Max Scaling, sliding window construction, and data splitting into training and testing sets. Model optimization was performed by evaluating combinations of hyperparameters, including the number of neurons, epochs, batch size, and dropout. Model performance was evaluated using Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The experimental results show that the optimal hyperparameter configuration achieved an RMSE of 83.99 and a MAPE of 2.41%. These results indicate that the BiLSTM model is capable of capturing the weekly gold price movement patterns in the dataset and can be used as an approach for deep learning-based price forecasting.
Referensi
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