Multivariate LSTM for drug purchase prediction in pharmaceutical management

Brawijaya, Fanny, Almais, Agung Teguh Wibowo and Chamidy, Totok (2025) Multivariate LSTM for drug purchase prediction in pharmaceutical management. Journal of Information Systems and Informatics (ISI), 7 (4). pp. 3502-3521. ISSN 2656-5935; E-ISSN 2656-4882

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Abstract

This study aims to develop a structured approach to predict the number of hospital drug purchases using deep learning techniques. The Multivariate Long Short-Term Memory (LSTM) model is designed to capture temporal and contextual patterns including transaction time, polyclinic type, and drug type to improve the efficiency of pharmaceutical management. The model was tested using outpatient transaction data at RSIA Fatimah Probolinggo hospital in East Java, Indonesia, through three configurations (A, B, and C) to determine the optimal parameters. The best model, the Model B1, produces a Mean Absolute Error (MAE) value of 10.239, Mean Absolute Percentage Error (MAPE) of 1.976%, and the Coefficient of Determination (R²) of 0.199, which indicates a high degree of accuracy. The results of the study prove that multivariate LSTM is able to model complex intervariable dependencies and provide superior results than conventional forecasting methods. In practical terms, this model can be used as a decision-making tool for hospital management in planning drug procurement, optimizing inventory, and preventing shortages and overstocks. The application of this model contributes to data-driven pharmaceutical supply chain planning in smart hospital management systems.

Item Type:	Journal Article
Keywords:	multivariate LSTM; drug purchase prediction; pharmaceutical management; deep learning; hospital procurement
Subjects:	08 INFORMATION AND COMPUTING SCIENCES > 0899 Other Information and Computing Sciences > 089999 Information and Computing Sciences not elsewhere classified
Divisions:	Faculty of Technology > Department of Informatics Engineering
Depositing User:	Totok Chamidy
Date Deposited:	10 Jul 2026 14:19

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