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Learning the Qur'an is a necessity for a Muslim, because the Qur'an acts as a guide
and way of life. One thing that is learned in the Qur'an is how to pronounce Hijaiyah letters or
Makharijul letters. In learning Makharijul Letters it takes an ustaz or accompanying teacher who
is limited by distance and time. To overcome the limitations of distance and time, a learning
application model is needed that can be accessed without the limitations of distance and the
time. This is studied aim to develop the Hijaiyah letter recognition model using the Mel
Frequency Cepstrum Coefficient (MFCC) and Convulational Neural Network (CNN) methods.
Based validation results, the model built using the MFCC and CNN methods can identify the
letters read with an accuracy of 49.1%. Then based of the test result with Confusion Matrix, this
model have a precision value of 50%, recall is 53%, and F-Measure is 0.514.
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