Komparasi Metode Klasifikasi Batik Menggunakan Neural Network Dan K-Nearest Neighbor Berbasis Ekstraksi Fitur Tekstur

  • Badroe Zaman Universitas Semarang, Indonesia
  • Ahmad Rifai Universitas Semarang, Indonesia
  • Mohammad Burhan Hanif Universitas Semarang, Indonesia
DOI: 10.51519/journalisi.v3i4.213
Keywords: Batik Tulis, Ekstraksi Fitur Tekstur, Backpropagation Neural Network, K-Nearest Neighbor, Klasifikasi

Abstract

Batik tulis adalah hasil seni budaya yang memiliki keindahan visual dan mengandung makna filosofis pada setiap motifnya. Batik tulis memiliki morif yang sangat beragam dan memiliki tingkat kompleksitas yang tingi sehingga menjadi kesulitan tersendiri dalam pengelompokan kelas batik tertentu. Klasifikasi citra ke dalam kelas tertentu juga menjadi permasalahan yang pelik dalam bidang pengenalan pola. Metode machine learning dapat digunakan untuk mengenali kelas batik melalui pengenalan citra batik. Namun belum banyak penelitian terkait studi komparasi klasifikasi citra batik. Sehingga penelitian ini berfokus pada data set citra batik tulis yang menggunakan dua motif yaitu motif klasik dan motif kontemporer. Pada penelitian ini, fitur ekstraksi menjadi dasar klasifikasi dengan metode Backpropagation Neural Network dan k-Nearest Neighbor. Tujuan dari penelitian ini untuk menemukan pola baru dalam data dengan menghubungkan pola data yang sudah ada dengan data yang baru. Selanjutnya, penelitian ini melakukan perbandingan metode klasifikasi antara Backpropagation Neural Network dan k-Nearest Neighbor untuk mencari metode klasifikasi terbaik untuk klasifikasi Batik tulis Bakaran. Hasil dari studi komparasi menunjukkan bahwa metode Backpropagation Neural Network memperoleh nilai akurasi 90,11% sedangkan metode k-Nearest Neighbor mendapatkan nilai akurasi 96,00%. Sehingga dapat di simpulkan bahwa metode k-Nearest Neighbor merupakan metode terbaik untuk klasifikasi citra batik.

Downloads

Download data is not yet available.

References

[1] UNESCO, “Indonesia Batik,” 2009. http://www.unesco.org/culture/ich/en/RL/indonesian-batik-00170 (accessed Sep. 21, 2021).
[2] Y. Sari, “Klasifikasi Pengenalan Motif Batik Berbasis Image Retrival,” Jukung (Jurnal Tek. Lingkungan), vol. 4, no. 2, pp. 27–33, 2018, doi: 10.20527/jukung.v4i2.6581.
[3] N. Luh and W. Sri, “Deteksi Batik Parang Menggunakan Fitur Co-Occurrence Matrix Dan Geometric Moment Invariant Dengan Klasifikasi KNN,” LONTAR Komput. VOL. 7, NO.1, April 2016, vol. 7, no. 1, pp. 715–725, 2016.
[4] K. A. N. Ignatia Dhian E.K.R, “Klasifikasi Batik Menggunakan KNN Berbasis Wavelet,” Semin. Nas. Teknol. Inf. dan Komun. 2016 (SENTIKA 2016), vol. Yogyakarta, no. ISSN : 2089-9815, pp. 615–623, 2016.
[5] T. Y. Prahudaya and A. Harjoko, “Metode Klasifikasi Mutu Jambu Biji Menggunakan Knn Berdasarkan Fitur Warna Dan Tekstur,” J. Teknosains, vol. 6, no. 2, p. 113, 2017, doi: 10.22146/teknosains.26972.
[6] A. E. Minarno, A. S. Maulani, A. Kurniawardhani, F. Bimantoro, and N. Suciati, “Comparison of methods for Batik classification using multi texton histogram,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 16, no. 3, pp. 1358–1366, 2018, doi: 10.12928/TELKOMNIKA.v16i3.7376.
[7] A. F. Achmalia, Walid, and Sugiman, “Peramalan Penjualan Semen Menggunakan Backpropagation,” UNNES J. Math., vol. 8, no. 1, pp. 92–106, 2019.
[8] B. Zaman and M. B. Hanif, “EKSTRAKSI FITUR BERBASIS WAVELET FILTER UNTUK MENINGKATKAN KINERJA NEURAL NETWORK PADA CITRA BATIK TULIS,” E-PROSIDING Semin. Nas. Has. Penelit. Lemb. Penelit. DAN Pengabdi. Kpd. Masy. Univ. SEMARANG, pp. 374–380, 2021.
[9] I. Nurhaida, H. Wei, R. A. M. Zen, R. Manurung, and A. M. Arymurthy, “Texture fusion for batik motif retrieval system,” Int. J. Electr. Comput. Eng., vol. 6, no. 6, pp. 3174–3187, 2016, doi: 10.11591/ijece.v6i6.12049.
[10] Y. Brasilka and D. Andreswari, “Klasifikasi Citra Batik Besurek Menggunakan Jaringan Syaraf Tiruan Self Organizing Map ( Som ),” vol. 3, no. 2, pp. 132–145, 2015.
[11] A. E. Minarno, Y. Munarko, A. Kurniawardhani, F. Bimantoro, and N. Suciati, “Texture Feature Extraction Using Co-Occurrence Matrices of Sub-Band Image For Batik Image Classification,” 2nd Int. Conf. Inf. Commun. Technol. Texture, pp. 249–254, 2014.
[12] Rangkuti, “Content Based Batik Image Classification using Wavelet Transform and Fuzzy Neural Network,” J. Comput. Sci., vol. 10, no. 4, pp. 604–613, Apr. 2014, doi: 10.3844/jcssp.2014.604.613.
[13] A. Kadir and A. Susanto, Pengolahan Citra. Yogyakarta, 2012.
[14] R. A. Pramunendar, C. Supriyanto, Dwi Hermawan Novianto, Ignatius Ngesti Yuwono, G. F. Shidik, and P. N. Andono, “A classification method of coconut wood quality based on Gray Level Co-occurrence matrices,” in 2013 International Conference on Robotics, Biomimetics, Intelligent Computational Systems, Nov. 2013, no. November, pp. 254–257, doi: 10.1109/ROBIONETICS.2013.6743614.
[15] C. S. K. Aditya, M. Hani’Ah, R. R. Bintana, and N. Suciati, “Batik classification using neural network with gray level co-occurence matrix and statistical color feature extraction,” Proc. 2015 Int. Conf. Inf. Commun. Technol. Syst. ICTS 2015, pp. 163–167, 2016, doi: 10.1109/ICTS.2015.7379892.
[16] A. E. Minarno, Y. Azhar, F. D. Setiawan Sumadi, and Y. Munarko, “A Robust Batik Image Classification using Multi Texton Co-Occurrence Descriptor and Support Vector Machine,” 2020 3rd Int. Conf. Intell. Auton. Syst. ICoIAS 2020, pp. 51–55, 2020, doi: 10.1109/ICoIAS49312.2020.9081833.
Published
2021-12-28
Abstract views: 2808 times
Download PDF: 2074 times
How to Cite
Zaman, B., Rifai, A., & Hanif, M. (2021). Komparasi Metode Klasifikasi Batik Menggunakan Neural Network Dan K-Nearest Neighbor Berbasis Ekstraksi Fitur Tekstur. Journal of Information Systems and Informatics, 3(4), 582-595. https://doi.org/10.51519/journalisi.v3i4.213
Issue
Vol 3 No 4 (2021): Journal of Information Systems and Informatics
Section
Articles
  1. I certify that I have read, understand and agreed to the Journal of Information Systems and Informatics (Journal-ISI) submission guidelines, policies and submission declaration. Submission already using the provided template.
  2. I certify that all authors have approved the publication of this and there is no conflict of interest.
  3. I confirm that the manuscript is the authors' original work and the manuscript has not received prior publication and is not under consideration for publication elsewhere and has not been previously published.
  4. I confirm that all authors listed on the title page have contributed significantly to the work, have read the manuscript, attest to the validity and legitimacy of the data and its interpretation, and agree to its submission.
  5. I confirm that the paper now submitted is not copied or plagiarized version of some other published work.
  6. I declare that I shall not submit the paper for publication in any other Journal or Magazine till the decision is made by journal editors.
  7. If the paper is finally accepted by the journal for publication, I confirm that I will either publish the paper immediately or withdraw it according to withdrawal policies
  8. I Agree that the paper published by this journal, I transfer copyright or assign exclusive rights to the publisher (including commercial rights)