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Kurniawati
Department of Informatics, Universitas Malikussaleh, Bukit Indah, Lhokseumawe, 24353, Indonesia, nurdin@unimal.ac.id
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Implementation of Triple Exponential Smoothing in Predicting Blood Stock Inventory Afif Diapari Ma'aruf Lubis Afif Diapari; Nurdin; Kurniawati
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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Abstract

Blood availability is an important component for the Indonesian Red Cross (PMI) Blood Donor Unit (UDD) in maintaining blood supplies so that blood is not wasted and there is no shortage. This study aims to test the effectiveness of using the Triple Exponential Smoothing (TES) method in predicting blood stock inventory at UDD PMI. Triple Exponential Smoothing is a forecasting method that considers seasonal patterns in data, which is relevant in predicting blood demand based on historical data. This study began by collecting historical blood stock data from January 2019 to December 2023. Next, the data was analyzed to identify seasonal patterns and trends. This method is applied to the four main blood types (A, B, AB, and O) by calculating the accuracy value using Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE). The results show that the TES method can accurately predict blood availability and demand, with a low MAPE value of 2.15% for blood type A. For blood type B, the MAPE value is 1.38%, blood type O is 1.03%, and blood type AB is 2.42%. This research is expected to significantly contribute to more effective and efficient bloodstock management at PMI and become an academic reference for future blood stock forecasting studies.
Co-Authors Afif Diapari Ma'aruf Lubis Afif Diapari Nurdin