Daftar Isi:
  • This classification of human blood group is widely used in hospital laboratory for research and treatment. The classification has not been implemented maximally and has slow progress. This happens because the analyst has not been standardized and the process was slow. The analysts were overhelmed in carrying out their duties. This study used radial basis function & backpropagation algorithm. Before the introduction was carried out, the feature extraction process carried out first using a projction histogram. Furthermore, the results was inserted into BackPropagation neural network for the training procces until the minimum of error. Based on error (galat) should being classification of blood groups with the accuracy reaches 96.12%.