INDENTIFIKASI POLA AKSARA ARAB MELAYU DENGAN JARINGAN SYARAF TIRUAN CONVOLUTIONAL NEURAL NETWORK (CNN) - Yanto, Budi; -, Basorudin; -, Jufri; Hayadi, B.Herawan (JSAI (Journal Scientific and Applied Informatics), 2020)
Riau province has Malay Arabic script as a traditional cultural heritage of ancient characters that should be preserved; this script is adapted from Arabic writing. This script from Malay Arabic has a unique form that is different from the original Arabic writing adaptation, which is read in a combination of letters forming latin meanings as an introduction to the everyday language of Riau Malay people in the earlier kingdom. Malay Arabic writing became an introduction to the local content of traditional languages in schools. To foster a love for preserving culture, in accordance with current technology that is able to recognize scripting patterns when written in paper, a knowledge base was created by using Matlab software by applying a convolutional Neural Network (CNN) artificial neural network algorithm capable of recognizing script patterns well. The result of image input in the form of handwriting written on paper then in the scanner in the form of JPEG image format. Testing was carried out on four Arabic Malay characters namely alif, ha, la, kho and nun. The result of training for the letter alif (a) epoch is obtained 98 out of 100 iterations with a training length of 3 seconds, furthermore, in validation performance with a result of 0.25013 on epoch 92 of 98 epoch for gradient letters with a value of 0.0071991 on the next epoch 98 in the extras produces an accuracy value of 0.6548 which states the correct result accordingness because it is close to the alif script. In the process of train input the letter kho obtained epoch 80 out of 100 iterations with a training process for 3 seconds, validation performance 0.25153 on epoch 74 out of 80 epoch for check validation with a value of 0.0011682 on the next epoch 80 in the extras obtained an extra value of 0.9326 stated the value is incorrect. Because the result of the extras results in an image that does not come close to the kho letter. Therefore, a study of how the system can recognize Malay Arabic writing patterns with the Convolutional Neural Network (CNN) method because it is very good at identifying image pattern features with an accuracy value of 4.12% of the 10 sample image patterns that have been inputted. With the introduction of imagery patterns from the extraction of features scanned Malay Arabic characters can help the findings of ancient Malay Arabic script as morphological learning of the validity of abstraction of Malay Arabic script is good