Klasifikasi Batik Pekalongan Berdasarkan Citra dengan Metode GLCM dan JST Backpropagation
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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.
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Wulandari, A. (2022). Batik Nusantara: Makna filosofis, cara pembuatan, dan industri batik. Penerbit Andi.
Harlina, T., & Handayani, E. (2022). Klasifikasi Motif Batik Banyuwangi Menggunakan Metode K-Nearest Neighbor (K-NN) Berbasis Android. JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), 7(1), 82-96. DOI: https://doi.org/10.29100/jipi.v7i1.2411.
Putri, R. A., & Rochmawati, N. (2019). Penerapan Algoritma Support Vector Machine untuk Klasifikasi Motif Citra Batik Solo Berdasarkan Fitur Multi-Autoencoders. Journal of Informatics and Computer Science (JINACS), 1(01), 56-63. DOI: https://doi.org/10.26740/jinacs.v1n01.p56-63.
Hardiyanto, D., Kristiyana, S., Kurniawan, D., & Sartika, D. A. (2019). Klasifikasi Motif Citra Batik Yogyakarta Menggunakan Metode Adaptive Neuro Fuzzy Inference System. Setrum: Sistem Kendali-Tenaga-elektronika-telekomunikasi-komputer, 8(2), 229-237.
Salamah, U. G., & Ekawati, R. (2021). Pengolahan Citra Digital. Media Sains Indonesia.
Sela, E. I. (2021). Deteksi osteoporosis pada citra radiograf panoramik dental menggunakan algoritme J48 dan learning vector quantization. Jurnal Teknologi Dan Sistem Komputer, 9(4), 211-217. https://doi.org/10.14710/jtsiskom.2021.14197.
Nasution, D. A., Khotimah, H. H., & Chamidah, N. (2019). Perbandingan normalisasi data untuk klasifikasi wine menggunakan algoritma K-NN. CESS (Journal of Computer Engineering, System and Science), 4(1), 78-82. DOI: https://doi.org/10.24114/cess.v4i1.11458.
ROCHMAN, E. M. S., & RACHMAD, A. (2021). Kecerdasan Komputasional: Konsep dan Aplikasi. Media Nusa Creative (MNC Publishing).
Wadi, H. (2021). Klasifikasi Citra Dengan Jaringan Syaraf Tiruan Backpropagation Menggunakan PYTHON GUI. Turida Publisher.