A Hybrid SARIMAX-SVR Model for Drug Demand Prediction

Rithagia Palelleng, Novy N. R. A. Mokobombang

2025

Abstract

Accurate prediction of drug demand is a critical challenge in Indonesia’s National Health Insurance (JKN) system, where misestimation can lead to two major problems: overstock, resulting in financial inefficiency, and stockouts, which directly disrupt patient therapy. Traditional time-series models such as ARIMA and SARIMA are often insufficient to capture nonlinear fluctuations and complex seasonal patterns in drug demand. To address this issue, this study proposes a hybrid prediction model that integrates the Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) and Support Vector Regression (SVR). The SARIMAX component captures linear trends and seasonality by incorporating external factors such as patient volume and e-catalog procurement status, while the SVR component corrects nonlinear residual patterns. The dataset consisted of daily drug consumption records, patient visits, and procurement data from the Dr. Tadjuddin Chalid Makassar Hospital. The Experimental results demonstrate that the hybrid SARIMAX–SVR model reduces the Root Mean Square Error (RMSE) by approximately 44% compared to standalone SARIMAX, across different drug demand categories. This approach provides a more adaptive, accurate, and computationally efficient forecasting framework to support decision-making in pharmaceutical supply chain management under the JKN scheme.

Paper Citation

in Harvard Style

Palelleng R. and Mokobombang N. (2025). A Hybrid SARIMAX-SVR Model for Drug Demand Prediction. In Proceedings of the 1st International Conference on Research and Innovations in Information and Engineering Technology - Volume 1: RITECH; ISBN 978-989-758-784-9, SciTePress, pages 209-216. DOI: 10.5220/0014272400004928

in Bibtex Style

@conference{ritech25,
author={Rithagia Palelleng and Novy N. R. A. Mokobombang},
title={A Hybrid SARIMAX-SVR Model for Drug Demand Prediction},
booktitle={Proceedings of the 1st International Conference on Research and Innovations in Information and Engineering Technology - Volume 1: RITECH},
year={2025},
pages={209-216},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0014272400004928},
isbn={978-989-758-784-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Innovations in Information and Engineering Technology - Volume 1: RITECH
TI - A Hybrid SARIMAX-SVR Model for Drug Demand Prediction
SN - 978-989-758-784-9
AU - Palelleng R.
AU - Mokobombang N.
PY - 2025
SP - 209
EP - 216
DO - 10.5220/0014272400004928
PB - SciTePress