  There are several training algorithms for backpropagation method in neural
network. Not all of these algorithms have the same accuracy level demonstrated
through the percentage level of suitability in recognizing patterns in the
data. In this research tested 12 training algorithms specifically in recognize
data patterns of test validity. The basic network parameters used are the
maximum allowable epoch = 1000, target error = 10-3, and learning rate = 0.05.
Of the twelve training algorithms each performed 20 times looping. The test
results obtained that the percentage rate of the great match is trainlm
algorithm with alpha 5% have adequate levels of suitability of 87.5% at the
level of significance of 0.000. This means the most appropriate training
algorithm in recognizing the the data pattern of test validity is the trainlm
algorithm.

Neural and Evolutionary Computing