<![CDATA[Speech recognition is a process of voice identification using specific parameters taken by the sound catcher. The development of technology gave rise to an event that requires a calculation model on a computer system in speech recognition to be useful in science. One of the computer systems is the Backpropagation Artificial Neural Network (JST). This research uses the Backpropagation method in human speech recognition with the aim of knowing the architecture model and the level of accuracy obtained. Linear Predictive Coding (LPC) is used for voice feature extraction. Voice features in the time domain are converted into the frequency domain using Fast Fourier Transform (FFT). The voice data was divided into 80% training data and 20% testing data. A suitable JST architecture model is selected through training by calculating the optimal weights and biases to recognize the voice patterns well. The best architecture model found was 64-15-1-1. The model was tested using test data to test its ability to recognize voice patterns. Evaluation was done using K-Fold Cross Validation to measure the accuracy of the model. The accuracy value against the training data is 0.95, while against the testing data is 0.088886. The JST architecture model is very good at recognizing voices in training data, but less good in testing. Hopefully, this method can help in the research process related to recognition.]]>
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