IMPLEMENTATION OF THE FUZZY TIME SERIES METHOD FOR FORECASTING BLOOD NEEDS IN THE INDONESIAN RED CROSS (PMI) MEDAN

Rina Syafiddini Harahap; Rakhmat Kurniawan R

doi:10.33387/jiko.v7i2.8614

IMPLEMENTATION OF THE FUZZY TIME SERIES METHOD FOR FORECASTING BLOOD NEEDS IN THE INDONESIAN RED CROSS (PMI) MEDAN

Rina Syafiddini Harahap, Rakhmat Kurniawan R

Abstract

The primary issue faced by PMI (Indonesian Red Cross) about blood requirements is often associated with insufficient blood supplies to satisfy the demand of patients, particularly during emergencies or significant catastrophes such as natural calamities. Hence, it is essential to use appropriate methodologies to forecast blood requirements accurately and determine the quantity of blood bags required in the future. When forecasting calculations using fuzzy time series, the interval length is established at the start of the calculation procedure. The duration of the gap significantly affects the establishment of fuzzy associations, which in turn affects the difference in forecast computation outcomes. The investigation reveals that Group AB has the lowest Root Mean Square Error (RMSE) value of 136.90, indicating that your model demonstrates superior accuracy in predicting blood group AB compared to other blood groups. The RMSE score for Group O is 819.5, which suggests that your model's accuracy in predicting blood group O is lower compared to other blood groups

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DOI: https://doi.org/10.33387/jiko.v7i2.8614

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