Proceedings of the 7th Mathematics, Science, and Computer Science Education International Seminar, MSCEIS 2019, 12 October 2019, Bandung, West Java, Indonesia

Research Article

The Artificial Neural Networks (ANN) for Batik Detection Based on Textural Features

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  • @INPROCEEDINGS{10.4108/eai.12-10-2019.2296538,
        author={Anita Ahmad Kasim and Muhammad  Bakri and Anindita  Septiarini},
        title={The Artificial Neural Networks (ANN) for Batik Detection Based on Textural Features},
        proceedings={Proceedings of the 7th Mathematics, Science, and Computer Science Education International Seminar, MSCEIS 2019, 12 October 2019, Bandung, West Java, Indonesia},
        publisher={EAI},
        proceedings_a={MSCEIS},
        year={2020},
        month={7},
        keywords={artificial neural networks (ann) batik detection textural features},
        doi={10.4108/eai.12-10-2019.2296538}
    }
    
  • Anita Ahmad Kasim
    Muhammad Bakri
    Anindita Septiarini
    Year: 2020
    The Artificial Neural Networks (ANN) for Batik Detection Based on Textural Features
    MSCEIS
    EAI
    DOI: 10.4108/eai.12-10-2019.2296538
Anita Ahmad Kasim1,*, Muhammad Bakri2, Anindita Septiarini3
  • 1: Departemen of Information Technology, Tadulako University, Palu, Indonesia
  • 2: Department of Architecture, Tadulako University, Palu, Indonesia
  • 3: Department of Computer Science, Mulawarman University, Samarinda, Indonesia
*Contact email: nita.kasim@gmail.com

Abstract

This study aims to utilize artificial neural networks to distinguish batik motifs and non-batik fabric motifs. Several important steps are needed, namely the process of acquiring batik and non-batik images, pre-transforming batik and non-batik images to gray scale forms, texture feature extraction in gray scale images and detection of motifs using networks artificial nerve. Image acquisition is done by collecting batik and not batik images from several different motifs. Processing data sets is divided into 70% as training data and 30% as testing data. Artificial neural network models used in this research use the Backpropagation learning algorithm by comparing the Scaled conjugate gradient algorithm (trainscg) training method and the Levenberg-Marquardt algorithm (trainlm) training method. The results obtained for the accuracy using the Scaled conjugate gradient algorithm (trainscg) training method were higher with an accuracy value of 84.12%, compared to the Levenberg-Marquardt algorithm (trainlm) method by 86.11%.