@phdthesis{digilib47014,
           month = {June},
           title = {PENERAPAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK
UNTUK PENGENALAN KARAKTER TULISAN TANGAN},
          school = {UIN SUNAN KALIJAGA YOGYAKARTA},
          author = {NIM.  17106050014 SETO RAHARDYANTO},
            year = {2021},
            note = {Dr. Sofwatul 'Uyun, S.T., M.Kom},
        keywords = {convolutional neural network, preprocessing, handwriting},
             url = {https://digilib.uin-suka.ac.id/id/eprint/47014/},
        abstract = {This study aims to develop a Convolutional Neural Network (CNN)
Model for the imposition of handwritten characters. Old documents that have
important information are usually easily damaged because they are stored on paper
media. The role of handwriting recognition is to convert the information contained
in the document into a digital format that can be stored for a long time.
This study uses 3 types of data, namely training data, validation data
and test data. Training data and test data are obtained from the Kaggle public
dataset. While the test data is made by the author himself. The training data and
validation data are then used to develop the CNN model, and the test data is
preprocessed to produce images that are ready to be recognized by the CNN model.
The test results show that the best CNN Model Architecture for
alphabetic handwritten character recognition is the Fourth Scenario CNN
Architecture Model which obtained validation data accuracy of 96.47\% and test
data accuracy (alphabet) of 93.05\%. The best CNN Model Architecture for
recognizing alphabet and numeric handwritten characters is the Third Scenario
CNN Architecture Model which obtained validation data accuracy of 94.70\% and
test data accuracy (alphabets and numbers) of 81.28\%. A test data preprocessing
algorithm was also developed which succeeded in producing 93.18\% of the images
recognized in the alphabetic test data image, and succeeded in producing 93.63\%
of the recognized images in the alphabetic and numeric test data images.}
}