Artificial Neural Network Based Prediction Model Back Propagation on Blood Demand and Blood Supply

TEDY, Frengky and BATARIUS, Patrisius and SAMANE, Ign. Pricher A. N. and SINLAE, Alfry Aristo Jansen (2023) Artificial Neural Network Based Prediction Model Back Propagation on Blood Demand and Blood Supply. JURNAL RESTI (Rekayasa Sistem dan Teknologi Informasi), 7 (6). 1403 -1411. ISSN 2580-0760

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Abstract

The balance between blood demand and supply at the Indonesian Red Cross Blood Transfusion Unit (UTD-PMI) is crucial. This condition needs to be maintained to reduce unused or expired blood supplies. Despite the situation at UTD-PMI, where the blood supply exceeds the demand, there is still a shortage of blood when needed by patients. This research aims to model the prediction of blood demand and supply for each blood type using the Back Propagation artificial neural network approach. Data from the last 3 years, from 2020 to 2022, were utilized in this research process. There are three stages in this research process. The first stage involves the training process, using data from January 2020 to December 2021. The testing process utilizes data from January 2021 to December 2022. The prediction process involves displaying forecasted data for the next 12 months from January to December 2023. The accuracy of the calculations is assessed using the Mean Square Error (MSE). Ultimately, the research results present the prediction model for the four blood types regarding blood demand and supply. These findings can serve as a reference for regulating future blood donation activities carried out by the UTD-PMI.

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