Implementation of LSTM for Gold Price Prediction in Indonesia - Sibannang, Maria Oktaviani Giska; Wijayakusuma, I Gusti Ngurah Lanang (Journal of Applied Informatics and Computing, 2026)
Gold is a significant investment instrument that serves as a safe-haven asset; nevertheless, its price dynamics are inherently nonlinear and highly volatile due to the influence of various economic factors. This study aims to develop a predictive model for daily gold prices denominated in Indonesian Rupiah. The proposed methodology employs a Long Short-Term Memory (LSTM) neural network architecture. Historical gold price data covering the period from January 1, 2015, to October 1, 2025, were obtained from investing.com. The dataset underwent a preprocessing phase, which included normalization using the MinMaxScaler and the construction of input sequences with a sliding window of 60 time steps. The implemented LSTM model consists of two stacked layers, each comprising 16 units, and is equipped with a dropout rate of 0.2 as well as an early stopping mechanism to improve generalization and prevent overfitting. The evaluation results demonstrate that the proposed model achieved a Mean Absolute Percentage Error (MAPE) of 5.08% and an accuracy of 94.92%, with a Mean Squared Error (MSE) of 0.00203. Furthermore, the visualization of prediction outcomes confirms the model’s capability to effectively capture actual price fluctuations, including during periods of heightened market volatility. Overall, these findings indicate that a relatively simple LSTM architecture is effective for forecasting gold price movements in the Indonesian market. The results of this study provide a robust foundation for the future development of more sophisticated predictive systems and potential real-time applications.