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Vol. 5 No. 1 (2024) • Articles
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Klasifikasi Batik Pekalongan Berdasarkan Citra dengan Metode GLCM dan JST Backpropagation

Fathul Am
Universitas Teknologi Yogyakarta
Enny Itje Sela
Universitas Teknologi Yogyakarta
Published: January 10, 2024 Pages: 614-621
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Abstract

Batik is an Indonesian cultural heritage that is internationally recognized by UNESCO. However, knowledge about the types of batik, especially traditional Pekalongan batik, is increasingly forgotten due to globalization. This research aims to create a Pekalongan traditional batik image classification system through Gray Level Co-Occurrence Matrix (GLCM) feature extraction and Artificial Neural Network (ANN) classification method. This system aims to make it easier for people to identify Pekalongan batik motifs without requiring special skills. The results showed that the GLCM and JST methods can be used to classify Pekalongan batik can predict correctly. The use of JST Backpropagation architecture with 3 hidden layers resulted in train data accuracy of 46.6% and test data accuracy of 55.5%. This system is expected to help preserve the cultural heritage of batik and increase public understanding of Pekalongan batik motifs.

Keywords:
Batik Artificial Neural Network Backpropagation Gray Level Co-Occurrence Matrix (GLCM)
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Author Biographies
Fathul Am Universitas Teknologi Yogyakarta

Program Studi Informatika, Fakultas Sains & Teknologi, Universitas Teknologi Yogyakarta, Kabupaten Sleman, Provinsi Daerah Istimewa Yogyakarta, Indonesia

Enny Itje Sela Universitas Teknologi Yogyakarta

Program Studi Informatika, Fakultas Sains & Teknologi, Universitas Teknologi Yogyakarta, Kabupaten Sleman, Provinsi Daerah Istimewa Yogyakarta, Indonesia

How to Cite
Fathul Am, & Sela, E. I. (2024). Klasifikasi Batik Pekalongan Berdasarkan Citra dengan Metode GLCM dan JST Backpropagation. Jurnal Indonesia : Manajemen Informatika Dan Komunikasi, 5(1), 614-621. https://doi.org/10.35870/jimik.v5i1.532
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References
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