Klasifikasi Motif Citra Batik Menggunakan Convolutional Neural Network Berdasarkan K-means Clustering
DOI:
https://doi.org/10.30865/mib.v5i4.3246Keywords:
Convolutional Neural Network, Citra Batik Yogyakarta, K-means Clustering, Median Filter, SharpeningAbstract
Batik has several motifs and patterns so it is necessary to identify certain objects in an image, one of which is the recognition of the image of Yogyakarta batik using the Convolutional Neural Network (CNN) method which is already popular in the use of image data classification. The introduction of batik imagery aims to contribute to the digitization of batik image data and at the same time provide information on types of batik to the public. The batik image recognition process using CNN in this study combines the image segmentation process and the enhancement process with median filters and sharpening. The segmentation process carried out before CNN aims to help separate foreground objects from objects that are not needed in the background. The segmentation process that is commonly used is using K-means Clustering. Where K-means Clustering is used to group data in the same category. Furthermore, the enhancement process using the median filter and sharpening was carried out separately to compare the batik image classification process using CNN based on K-means Clustering from the median filter results and the sharpening results. The batik image classification process with CNN based on K-means Clustering on the median filter resulted in an accuracy value of 100%. Meanwhile, the batik image classification process with CNN based on K-means Clustering from the sharpening results resulted in an accuracy value of 80%.References
A. Wulandari, Batik Nusantara: makna filosofis, cara pembuatan dan industri batik. Yogyakarta: Andi, 2011.
L. M. Hakim, “Batik Sebagai Warisan Budaya Bangsa dan Nation Brand Indonesia,†Nation State J. Int. Stud., vol. 1, no. 1, pp. 61–90, 2018.
A. Kratonjogja, “Warisan Budaya Tak Benda: Batik Gaya Yogyakarta,†www.kratonjogja.id/tak-benda/Motif%20Batik/17/batik-gaya-yogyakarta#, 2018. [Online]. Available: https://www.kratonjogja.id/tak-benda/Motif Batik/17/batik-gaya-yogyakarta#.
I. Nurhaida, A. Noviyanto, R. Manurung, and A. M. Arymurthy, “Automatic Indonesian’s Batik Pattern Recognition Using SIFT Approach,†Procedia Comput. Sci., vol. 59, no. Iccsci, pp. 567–576, 2015.
P. Chyan and S. C. Sumarta, “Penerapan Image Enhancement Algorithm Untuk Meningkatkan Kualitas Citra Tak Bergerak,†no. September, 2017.
S. S. Sumijan, A. W. Purnama, and S. Arlis, “Peningkatan Kualitas Citra CT-Scan dengan Penggabungan Metode Filter Gaussian dan Filter Median,†J. Teknol. Inf. dan Ilmu Komput., vol. 6, no. 6, p. 591, 2019.
Khairunnisa and C. F. Sianturi, “Perbaikan Kualitas Citra Menggunakan Metode High Pass Filter dan Sharpening,†J. Multimed. dan Teknol. Inf., vol. 2, 2020.
A. Y. Wicaksono, N. Suciati, C. Fatichah, K. Uchimura, and G. Koutaki, “Modified Convolutional Neural Network Architecture for Batik Motif Image Classification,†IPTEK, J. Sci., vol. 2, no. 1, pp. 26–30, 2017.
C. N. Prabiantissa, A. Retno, T. Hayati, and R. A. Asmara, “Sistem Identifikasi Batik Alami Dan Batik Sintetis Berdasarkan Karakteristik Warna Citra Dengan Metode K-Means Clustering,†vol. 3, pp. 26–31, 2017.
S. F. Tumewu, D. H. Setiabud, and I. Sugiarto, “Klasifikasi Motif Batik menggunakan metode Deep Convolutional Neural Network dengan Data Augmentation,†J. Infra, vol. 8, no. 2, pp. 189–194, 2020.
T. Bariyah, M. A. Rasyidi, and N. Ngatini, “Convolutional Neural Network untuk Metode Klasifikasi Multi-Label pada Motif Batik,†Techno.Com, vol. 20, no. 1, pp. 155–165, 2021.
R. Mawan, “Klasifikasi motif batik menggunakan Convolutional Neural Network,†Jnanaloka, pp. 45–50, 2020.
A. Minarno, “Klasifikasi Jenis Batik Menggunakan Algoritma Convolutional Neural Network,†J. Repos., vol. 3, Mar. 2021.
R. Munir, Pengolahan Citra Digital Dengan Pendekatan Algoritmik. Bandung: Informatika, 2004.
A. Wedianto, H. L. Sari, and Y. S. H, “Analisa Perbandingan Metode Filter Gaussian, Mean Dan Median Terhadap Reduksi Noise,†J. Media Infotama, vol. 12, no. 1, pp. 21–30, 2016.
H. A. Siregar, “Implementasi Metode Sharpening Untuk Memperbaiki Kualitas Citra,†J. Pelita Inform., vol. 8, pp. 5–8, 2019.
Gustientiedina, M. H. Adiya, and Y. Desnelita, “Penerapan Algoritma K-Means Untuk Clustering Data Obat-Obatan,†J. Nas. Teknol. dan Sist. Inf., vol. 5, no. 1, pp. 17–24, 2019.
R. F. Mayoza, “Menentukan Tingkat Kematangan Buah Pisang dengan Segmentasi Warna Kulit Pisang Menggunakan K-Means,†Open Access Procedings J. Phys. Conf. Ser., 2019.
M. Nielsen, “Neural Networks and Deep Learning,†Determ. Press, 2015.
I. W. S. Eka Putra and R. Soelaiman, “Klasifikasi Citra Menggunakan Convolutional Neural Network (CNN) pada Caltech 101,†J. Tek. ITS, vol. 5, no. 1, 2016.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
