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All Journal Advance Sustainable Science, Engineering and Technology (ASSET)
Albi Pernata Jomantara Putra
Telkom University
Chemistry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Comparative Deep Learning Models for Indonesian Gold Price Forecasting Albi Pernata Jomantara Putra; Baginda Mi’raj Williamsyah; Achmad Rizal; Favian Dewanta; Anggunmeka Luhur Prasasti; Said Ziani
Advance Sustainable Science Engineering and Technology Vol. 8 No. 3 (2026): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i3.2608

Abstract

This study evaluates LSTM, CNN-LSTM, LSTM-GRU, and CNN-LSTM-GRU architectures for forecasting Indonesian gold prices using 1,269 daily observations (2022–2025). Models utilized Bayesian-optimized hyperparameters and were benchmarked against ARIMA-GARCH and Random Forest baselines across 30-day and 365-day horizons. Performance was assessed via MAE, RMSE, R², and MAPE, confirming deep learning’s superiority in capturing non-linear dynamics over classical methods. The LSTM-GRU achieved the best numerical results, with MAPEs of 1.21% (short-term) and 1.32% (long-term). However, qualitative evaluation revealed that the highest-scoring model produced unstable long-term predictions, indicating a critical trade-off between numerical accuracy and forecast realism. These findings suggest financial model selection must prioritize stability alongside statistical metrics. A key limitation is the exclusive use of univariate data, necessitating future multivariate validation with macroeconomic indicators. 
Co-Authors Achmad Rizal ANGGUNMEKA LUHUR PRASASTI Baginda Mi’raj Williamsyah Favian Dewanta Said Ziani