PENGENALAN POLA HANDWRITINGCITRA ANGKA ARAB MENGGUNAKAN JST BACKPROPAGATION

ANDI HAMDIANAH, NIM. 10651068 (2015) PENGENALAN POLA HANDWRITINGCITRA ANGKA ARAB MENGGUNAKAN JST BACKPROPAGATION. Skripsi thesis, UIN SUNAN KALIJAGA YOGYAKARTA.

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Abstract

Research on pattern recognition of handwriting is often done by various methods. Methods most commonly used in the classification that is backpropagation. This method is believed to be able to classify each class well, but the accuracy is highly influential in the pre-processing and feature extraction. As we know handwritten Arabic numerals have many variations writing. Pattern recognition handwritten Arabic numerals requires 207 sample image captured using a digital camera then stored in a computer with a file format jpg size 4320x3240 pixels. 207 samples were split into three parts, namely, 136 training data, validation data 36 and 36 test data. The whole sample through praprosesing and feature extraction using PCA. Identification using ANN Backpropagation to calculate the success rate in identifying the image. The test results ANN Backpropagation ie, 100% on the training data, 31% of the data validation, and 28% in the test data. Optimal architecture used two input nodes, three hidden layer, and output layer 4. The optimal parameters used are MSE 0.0001; learning rate of 0.1; 0.3 momentum; and epoch 100000. However, the results are lacking in recognizing handwriting Arabic numerals. From here the authors classify the data of feature extraction using ANOVA statistical techniques. The results show 72% have the same data variation. This suggests that, PCA feature extraction less represent traits in pattern recognition handwriting Arabic numerals.

Item Type: Thesis (Skripsi)
Additional Information: Dr. Shofwatul „Uyun, S.T., M.Kom.
Uncontrolled Keywords: ANN, Anova, Arabic numbers, Backpropagation, feature extraction, Handwriting, PCA.
Subjects: Tehnik Informatika
Divisions: Fakultas Sains dan Teknologi > Teknik Informatika (S1)
Depositing User: Miftahul Ulum [IT Staff]
Date Deposited: 04 Dec 2015 13:46
Last Modified: 04 Dec 2015 13:46
URI: http://digilib.uin-suka.ac.id/id/eprint/18546

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