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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan
Loshini Thiruchelvam
Department of Physical and Mathematical Science, Faculty of Science, Universiti Tunku Abdul Rahman, Malaysia
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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PREDICTING HOURLY AMBULANCE USAGE FOR TWO SELECTED GOVERNMENT HOSPITALS IN MALAYSIA; COMPARISON BETWEEN SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (SARIMA) AND SEASONAL DECOMPOSITION MODELS Loshini Thiruchelvam; Nur Balqishanis Zainal Abidin; Uma Eswari Punchanathan; Nur Dayana Abdul Rahman; Nur Amalina Mat Jan; Jane Irene P. J. Antony
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 4 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss4pp2799-2812

Abstract

An ambulance is a vehicle used to transport sick or injured people, and the patient will be sent to a hospital for further treatment. This study aims to predict hourly use of ambulances in two selected government hospital, namely the Hospital Sultanah Nur Zahirah (HSNZ), located in Terengganu, a state in the East Peninsular Malaysia, and Hospital Tapah, located in Perak state, in the West Peninsular Malaysia. Data on hourly ambulance usage were obtained from the hospital’s emergency department for 2010. Two distinct statistical models, namely the Seasonal Autoregressive Integrated Moving Average (SARIMA) and Seasonal Decomposition model, were used to model the dataset. The study found that both hospitals have different best forecasting models: HSNZ uses the Seasonal Decomposition Model, whereas the SARIMA model was found to forecast better at Hospital Tapah. For example, in the HSNZ study, the study determined the best SARIMA model from a few candidate models, namely. This model showed good performance on the training dataset and performed best on the test dataset. However, when further compared with the seasonal decomposition model, the study found the latter model to perform better. When visually investigated as well, the model’s forecasted values were close to the actual values, or at least relatively acceptable. So far, this study concludes that the developed model is highly localized and specific to the given dataset.
Co-Authors Jane Irene P. J. Antony Nur Amalina Mat Jan Nur Balqishanis Zainal Abidin Nur Dayana Abdul Rahman Uma Eswari Punchanathan