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All Journal Journal of Advanced Sciences and Mathematics Education
Moch Abdillah Nafis
Institut Teknologi Sepuluh Nopember, Indonesia
Chemistry Education Mathematics Physics Social Sciences

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Rock aggregate demand forecasting using long short-term memory optimized by particle swarm optimization Syarifuddin Ishak; Fitriah Fajar Maghfirah; Moch Abdillah Nafis; Haruna Lufni
Journal of Advanced Sciences and Mathematics Education Vol. 6 No. 3 (2026): Journal of Advanced Sciences and Mathematics Education
Publisher : CV. FOUNDAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/jasme.v6i3.1570

Abstract

Background: Accurate rock aggregate demand forecasting is essential for logistics planning and fleet allocation, yet highly fluctuating demand, missing observations, and non-linear temporal patterns can challenge conventional forecasting approaches. Aim: This study aimed to evaluate a computational forecasting approach using Long Short-Term Memory optimized by Particle Swarm Optimization (LSTM-PSO) and compare its predictive performance with Croston Syntetos-Boylan Approximation (SBA) and Auto-ARIMA. Method: Monthly rock aggregate demand from PT. Hasal Logam Utama covering January 2017–December 2021 was analyzed. Seven missing observations were treated using seasonal imputation and linear interpolation, and the resulting 60-month series was divided into 54 training and six testing observations. PSO optimized five LSTM hyperparameters, while model performance was assessed using sMAPE, MASE, RMSSE, and Forecast Accuracy (FA). Results: LSTM-PSO achieved an sMAPE of 11.83%, MASE of 0.1552, RMSSE of 0.1139, and FA of 88.12%, compared with Croston SBA (sMAPE=17.71%; FA=82.93%) and Auto-ARIMA (sMAPE=16.86%; FA=82.15%). Conclusion: LSTM-PSO demonstrated promising forecasting performance for the examined rock aggregate demand series, although validation using longer datasets and additional forecasting periods is required to establish broader generalizability. 
Co-Authors Fitriah Fajar Maghfirah Haruna Lufni Syarifuddin Ishak