Article Per Year (5 Year)
p-Index From 2021 - 2026
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All Journal Bulletin of Electrical Engineering and Informatics Advance Sustainable Science, Engineering and Technology (ASSET)
Said Ziani
Mohammed V 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. 
Artificial intelligence-based solar radiation forecasting for energy optimization in sustainable buildings Imad Laabab; Said Ziani; Abdellah Benami
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.11103

Abstract

The work presented in this article aligns with our university's commitment to advancing renewable energy sources. We can better plan and optimize energy use if we are aware of the factors that affect solar energy generation. This study examines the application of artificial neural networks (ANNs) in forecasting global horizontal irradiance (GHI) within the context of sustainable energy. The primary objective is to enhance the accuracy and reliability of solar irradiance forecasts, thereby improving the performance of renewable energy systems, such as concentrated solar power (CSP). This article provides an overview of solar radiation, the physical factors that influence its distribution, and the impact of panel tilt angle on energy production. It presents a case study in Morocco, which uses a hybrid approach to predict solar radiation. The results demonstrate that ANN, employing advanced machine learning (ML) methods, provides more accurate and reliable forecasts than traditional models. This advance could improve energy planning, reduce uncertainty, and enable better management of solar energy production and storage systems. Our results suggest that this approach has increased forecast accuracy.
Co-Authors Abdellah Benami Achmad Rizal Albi Pernata Jomantara Putra ANGGUNMEKA LUHUR PRASASTI Baginda Mi’raj Williamsyah Favian Dewanta Imad Laabab