Klasifikasi Calon Pendonor Darah Potensial Menggunakan Metode Pendekatan Support Vector Machine (SVM)

Blood donation or blood transfusion is a very important part of the human body as well as in terms of human blood grouping. Surgery, cancer, and trauma are the three leading causes of blood transfusion in affluent nations. In low-income nations, blood transfusions are typically needed to treat illnesses like pregnancy complications and pediatric anemia. By lowering the maternal death rate from 228/100,000 live births to 102/100,000 live births, Indonesia would be able to support the achievement of the 2010-2014 national health development goals and the Millennium Development Goals (MDGs). It has been noticed that 2.5% of Indonesia's overall population serves as the ideal blood supply for donors. The poor public knowledge of voluntary donors, among other factors, is a constraint on the availability of blood in UTD and contributes to the shortage of blood in Indonesia. The classification of potential blood donors can be done in this situation. The SVM (Support Vector Machine) method is utilized in this study to categorize donors based on whether or not they meet the requirements for giving blood. 100 data points were collected for the dataset directly from the UTD PMI Malang City office. The suggested model yields the best outcomes, with 15 results accepted and 3 rejected with an accuracy rate of 90% using the Confusion Matrix on 20 training data. The proposed SVM model is useful for categorizing possible blood donors, according to the test results