Pemanfaatan metode SMOTE untuk prediksi periode donor darah dengan metode Backpropagation - Auliansyah, Ach Afrian; Puspitasari, Willia Dwi; Wafiroh, Ni’matul (Maliki Interdisciplinary Journal, 2024)
Prediction of blood donor periods is an important part of blood storage to ensure an adequate blood supply. The aim of this research is to compare the performance of the SMOTE method with an approach that does not use oversampling on the blood donor period dataset. The dataset used in this research includes information about blood donors, which consists of 4 attributes and 1 label. The experimental results show that the use of the SMOTE method significantly improves the predictability of blood donor periods. SMOTE successfully improves the accuracy, precision, recall, and F1 score of the classification model used. This indicates that the use of oversampling can address the class imbalance issue in the blood donor period dataset and improve the predictive ability of the model. This research makes an important contribution to the field of knowledge engineering by applying the SMOTE method to predict blood donor periods combined with Backpropagation. The results of this research can serve as a guide for the development of more efficient and effective blood donor prediction systems.