Aplikasi Jaringan Syaraf Tiruan dalam Pengenalan Pola Huruf Pegon Jawa - Hari Surrisyad; Ahmad Subhan Yazid (UIN Sunan Kalijaga Yogyakarta, 2018)
Artificial Neural Network (ANN) Technology) can help humans in processing data into information with design resembling the performance of the human brain. ANN adopts 5 aspects of human capability: Memorization, Generalization, Efficiency, Accuracy, and Tolerance in its application. ANN proves to be effective in pattern recognition. Researchers developed an application implementing ANN to recognize Java Pegon Letter pattern. The research uses 160 image data, divided into 100 training data (consisting of 5 normal images for each character) and 60 test data (consisting of 1 normal data, 1 data is not complete/corrupt, and 1 data with noise) for each character. The data obtained from the processed captures, so all of data have the same dimensions and size: 100x100 pixels. All data is processed through preprocessing and extraction stages. Furthermore, the data result is used in training stage to recognize the pattern of Java Pegon by applying the Learning Vector Quantization method. The application can recognize Java pegon pattern very well. The application can recognize 100% of training data and test data. This application also has the ability to recognize abnormal data very well, such as data with noise or corrupted data.