International Journal of Computer Science & Network Security

국제컴퓨터통신보호논문지학회 (International Journal of Computer Science & Network Security)
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A Performance Comparison of Backpropagation Neural Networks and Learning Vector Quantization Techniques for Sundanese Characters Recognition

  • Haviluddin (Dept. of Informatics, Faculty of Engineering, Universitas Mulawarman) ;
  • Herman Santoso Pakpahan (Dept. of Informatics, Faculty of Engineering, Universitas Mulawarman) ;
  • Dinda Izmya Nurpadillah (Dept. of Informatics, Faculty of Engineering, Universitas Mulawarman) ;
  • Hario Jati Setyadi (Dept. of Informatics, Faculty of Engineering, Universitas Mulawarman) ;
  • Arif Harjanto (Dept. of Electrical, Faculty of Engineering, Universitas Mulawarman) ;
  • Rayner Alfred (Dept. of Software Engineering, Faculty of Computing and Informatics, Universiti Malaysia Sabah)
  • 투고 : 2024.03.05
  • 발행 : 2024.03.30
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초록

This article aims to compare the accuracy of the Backpropagation Neural Network (BPNN) and Learning Vector Quantization (LVQ) approaches in recognizing Sundanese characters. Based on experiments, the level of accuracy that has been obtained by the BPNN technique is 95.23% and the LVQ technique is 66.66%. Meanwhile, the learning time that has been required by the BPNN technique is 2 minutes 45 seconds and then the LVQ method is 17 minutes 22 seconds. The results indicated that the BPNN technique was better than the LVQ technique in recognizing Sundanese characters in accuracy and learning time.

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