Implementation of Deep Learning for Handwriting Imagery of Sundanese Script Using Convolutional Neural Network Algorithm (CNN)


Arif Purnama(1); Saeful Bahri(2); Gunawan Gunawan(3); Taufik Hidayatulloh(4*); Satia Suhada(5);

(1) Universitas Bina Sarana Informatika
(2) Universitas Nusa Mandiri
(3) Universitas Bina Sarana Informatika
(4) Universitas Bina Sarana Informatika
(5) Universitas Nusa Mandiri
(*) Corresponding Author

  

Abstract


Aksara Sunda becomes one of the cultures of sundanese land that needs to be preserved. Currently, not all people know Aksara Sunda because of the shift in cultural values and there is a presumption that Aksara Sunda is difficult to learn because it has a unique and complicated shape. The use of deep learning has been widely used, especially in the field of computer vision to classify images, one of the commonly used algorithms is the Convolutional Neural Network (CNN). The application of The Convolutional Neural Network (CNN) algorithm on sundanese handwriting imagery can make it easier for people to learn Sundanese script, this study aims to find out how accurate the neural network convolutional algorithm is in classifying Aksara Sunda imagery. Data collection techniques are done by distributing questionnaires to respondents. System testing using accuracy tests, testing on CNN models using data testing get 97.5% accuracy and model testing using applications get 98% accuracy. So based on the results of the trial, the implementation of deep learning methods using neural network convolution algorithms was able to classify the handwriting image of Aksara Sunda well.


Keywords


Deep Learning; Convolutional Neural Network; Computer Vision; Aksara Sunda; Classification of Images

  
  

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doi  https://doi.org/10.33096/ilkom.v14i1.989.10-16
  

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