<![CDATA[Hijaiyah letters are Arabic spelling letters that are the original language of the Qur'an. Just like other types of letters, the hijaiyah has certain shapes and characteristics that will form a certain pattern. By using the concept of artificial neural networks, can dibanguun a system that can recognize the pattern by doing the previous training. One of the most commonly used meotodes in artificial neural network paradigms is the crawling or backpropagation buffer. This hijaiyah letters identification system is built using the handwritten hijaiyah image data of 150 images. The feature or feature taken from the image is the binary value of the letter pattern and the number of objects contained in the letters. Prior to the feature extraction process, the image first passes the preprocessing stage consisting of color binerization, object widening, cropping, and resizing. The result obtained by backpropagation method is the system is able to recognize handwriting hijaiyah pattern well. All training data have been correctly identified, while as many as 150 test data can be identified as 77 letters with an accuracy of 51.33%. This accuracy value is obtained with the architectural arrangement of the number of hidden layer neurons = 60, minimum error = 0.001 and maximum iteration = 10000.keyword:backpropagation, biner, hijaiyah, , pattern, preprocessing]]>
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