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

Selection of Most Appropriate Backpropagation Training Algorithm in Data Pattern Recognition