Klasifikasi Persediaan Stok Darah Menggunakan Algoritma K-NN, Decision Tree, dan JST Backpropagation

The demand for blood is critical for various purposes, such as surgeries, transplants, cancer treatments, dialysis, and disaster victims. The availability of blood at the Blood Transfusion Unit (UTD) of the Indonesian Red Cross (PMI) is crucial, as a shortage of stock can endanger patients' lives. Therefore, this study aims to evaluate the condition of blood stock to determine whether it is safe or insufficient. This research focuses on comparing blood stock classification at PMI Kota Yogyakarta using three algorithms: K-Nearest Neighbor, Decision Tree, and Artificial Neural Network (Backpropagation). The study objects consist of 216 blood stock data point. Testing is conducted using the K-Fold Cross Validation method with a k value of 8 on 216 data points. The research results show that the K-Nearest Neighbors (KNN) algorithm achieves an Accuracy of 85.18%, Recall of 85.03%, Precision of 89.25%, F1-Score of 87.09%, and Specificity of 84.39%. The Decision Tree algorithm achieves an Accuracy of 84.72%, Recall of 88.18%, Precision of 86.15%, F1-Score of 87.15%, and Specificity of 78.08%. The Artificial Neural Network (Backpropagation) algorithm shows the best performance with an Accuracy of 93.05%, Recall of 96.06%, Precision of 92.42%, F1-Score of 94.20%, and Specificity of 89.35%. Thus, it can be concluded that the Artificial Neural Network (Backpropagation) algorithm outperforms the other algorithms in classifying blood stock availability. Keywords—PMI, Blood, Classification, K-Nearest Neighbor, Decision Tree, Backpropagation