(Department of Informatics Engineering, Universitas Malikussaleh, Lhokseumawe, 24352, Indonesia)
(2) Eva Darnila
(Department of Informatics Engineering, Universitas Malikussaleh, Lhokseumawe, 24352, Indonesia)(3) Lidya Rosnita
(Department of Informatics Engineering, Universitas Malikussaleh, Lhokseumawe, 24352, Indonesia)*Corresponding author
AbstractThis study aims to develop a blood bag inventory forecasting system at the Blood Donation Unit (UDD) of the Indonesian Red Cross (PMI) in Lhokseumawe City and the UDD of PMI North Aceh using the Random Forest (RF) algorithm. The background of this study focuses on the imbalance between demand and availability of blood bags, which can have serious implications for patient safety. Using historical data comprising 574 records from the period 2022 to 2025, this study employs a quantitative approach with variables such as month, year, PMI location, blood type, incoming blood, and blood distribution. The research methodology includes preprocessing and feature engineering steps, resulting in 17 features for training the Random Forest (RF) model. This process was completed with hyperparameter optimization using RandomizedSearchCV to improve model accuracy. The results show that the PMI UDD Aceh Utara achieved excellent performance with an RMSE of 9.113 for incoming blood and 8.750 for distribution. On the other hand, the UDD PMI Lhokseumawe had an RMSE of 24.635 and 22.737, respectively. Comprehensive predictions for the 2025-2030 period indicate an optimistic projection with 94% surplus months and a positive net balance of 4,406.7 bags. The implemented web-based system supports real-time forecasting and strategic decision-making in blood bag inventory management within the PMI organization.
KeywordsForecasting; Blood; Transfution; Stock; RF
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DOIhttps://doi.org/10.33122/ejeset.v6i2.1022 |
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