The absence of a neural network algorithm model to predict the level of accuracy in terms of black-box software testing, equivalence partitions technique is a problem in this research. In this case, the algorithm used for predicting software errors by researchers is the neural network algorithm and testing the software uses the black-box method with the equivalence partitions technique. The neural network algorithm is an artificial neural system, or neural network are the physical cellular system that can acquire, store and use the knowledge gained from experience for activation using bipolar sigmoid output values which range between -1 to 1. Software testing black-box methods is a testing approach where the data comes from defined functional requirements regardless of the final program structure, and the technique used is equivalence partitions. The design prediction accuracy of this research is by determining the college application to be the software to be tested, then tested using the black-box method with the equivalence partitions technique (this test chosen because it will find software errors in several categories, including functions error or missing, interface errors, errors in data structures or external database access, performance errors, initialization errors and terminations), from the black-box test the dataset obtained. This dataset measures the accuracy of the real output and prediction output. The last step is calculating the error, RSME from the real output and prediction output. The results of this research show that the neural network algorithm was being to measure the accuracy level of software testing applied to determine the prediction of the accuracy level of black-box software testing with the equivalence partitions technique, and the average accuracy results are above 80%.
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