Multivariate LSTM for Drug Purchase Prediction in Pharmaceutical Management

  • Fanny Brawijaya Universitas Islam Negeri Maulana Malik Ibrahim Malang, Indonesia
  • Agung Teguh Wibowo Almais Universitas Islam Negeri Maulana Malik Ibrahim, Indonesia
  • Totok Chamidy Universitas Islam Negeri Maulana Malik Ibrahim, Indonesia
DOI: 10.63158/journalisi.v7i4.1313
Keywords: Multivariate LSTM, Drug Purchase Prediction, Pharmaceutical Management, Deep Learning, Hospital Procurement

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.

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Published
2025-12-10
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How to Cite
Brawijaya, F., Almais, A. T., & Chamidy, T. (2025). Multivariate LSTM for Drug Purchase Prediction in Pharmaceutical Management. Journal of Information Systems and Informatics, 7(4), 3502-3521. https://doi.org/10.63158/journalisi.v7i4.1313
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Vol 7 No 4 (2025): December
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