Identification of Papua Cenderawasih Batik Motifs using Local Binary Pattern and K-Nearest Neighbor

Dian Dwi Ariani, Sitti Zuhriyah, Eva Yulia Puspaningrum, Mahabintang Pallawabonang

Abstract


Papua Island has natural and cultural richness wich is reflected in its batik motifs, such as the Cenderawasih and Tifa motifs. Although batik recognition technology has developed, systems capable of automatically identifying Papua batik motifs are still limited. This research aims to develop a texture recognition system using the Local Binary Pattern (LBP) feature extraction method and K-Nearest Neighbor (KNN) classification. The Cenderawasih motif dataset consists of 115 images, and the Tifa motif dataset consists of 120 images with an 80:20 composition for training and testing data. We tested the KNN model with various k values and found that k = 7 yielded the best results, with accuracy of 97.16%, precision of 97.10%, and F1-score of 97.10%. The developed GUI interface facilitates users in identifying batik motifs, providing prediction results, and texture visualization. The results of this study show that image processing technology could help protect Papuan batik. Future research could improve model accuracy by utilizing larger data sets and classification algorithms to make the models more accurate.

Keywords


Papuan batik motifs, Local Binary Pattern, K-Nearest Neighbor, image processing, classification.

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References


A. R. Rini, “Penerapan Teknik Bordir dengan Layering untuk membuat Efek 3D Burung Cendrawasih dan Penggunaan Motif Batik Papua pada Busana Ready-To-Wear Deluxe,” 2023.

M. T. Kanugroho, M. A. Rahman, And R. C. Wihandika, “Klasifikasi Batik dengan Ekstraksi Fitur Tekstur Local Binary Pattern dan Metode K-Nearest Neighbor,” 2022. [Online]. Available: Http://J-Ptiik.Ub.Ac.Id

S. Aras And A. Setyanto, “Deep Learning untuk Klasifikasi Motif Batik Papua menggunakan Efficientnet dan Trasnfer Learning,” J. Inform., Vol. 8, No. 1, 2022.

A. Akbar And D. I. Mulyana, “Optimasi Klasifikasi Batik Betawi menggunakan Data Augmentasi dengan Metode KNN Dan GLCM,” J. Apl. Teknol.Inf.Manaj. (Jatim), Vol. 3, No. 2, Pp. 92–101, 2022.

Z. Y. Lamasigi, “Dct untuk Ekstraksi Fitur berbasis GLCM pada Identifikasi Batik Menggunakan K-NN,” Jambura J. Electr. Electron. Eng., Vol. 3, 2021.

K. Widyatmoko, E. Sugiarto, And F. Budiman, “Optimasi Metode K-Nearest Neighbor dengan Particle Swarm Optimization untuk Pengenalan Citra Batik Ragam Hias Geometris,” J. Inform. Upgris, Vol. 8, No. 1, 2022.

P. Sulistiyawati, D. I. Ihya’ulumuddin, And A. P. Azhari, “Implementasi Komputer Grafis pada Perancangan Motif Batik Papua,” 2020.

A. I. Nurulrachman, R. C. Wihandika, And P. P. Adikara, “Ekstraksi Ciri pada Klasifikasi Citra Batik menggunakan Metode Gray Level Co-Occurrence Matrix, Local Binary Pattern, dan HSV Color Moment,” 2023. [Online]. Available: Http://J-Ptiik.Ub.Ac.Id

A. P. B. Salsabila, C. Rozikin, And R. I. Adam, “Klasifikasi Motif Batik Karawang berbasis Citra Digital dengan Principal Component Analysis dan K-Nearest Neighbor,” J. Syst. Teknol. Inf. (Justin), Vol. 11, No. 1, P. 20, Jan. 2023, Doi: 10.26418/Justin.V11i1.46936.

D. Sinaga And C. Jatmoko, “Klasifikasi Citra Batik Sumatera menggunakan Naïve Bayes berbasis Fitur Ekstraksi GLCM,” 2024.

R. Mawan, “Klasifikasi Motif Batik menggunakan Convolutional Neural Network,” 2020, Doi: 10.36802/Jnanaloka.

A. H. Rangkuti, A. Harjoko, And A. Putra, “A Novel Reliable Approach for Image Batik Classification that Invariant with Scale and Rotation using Mu2ecs-LBP Algorithm,” in Proc. Comput. Sci., Elsevier B.V., 2021, Pp. 863–870. Doi: 10.1016/J.Procs.2021.01.075.

P. Anisa And A. Rahmatulloh, “Classication of Batik Tasikmalaya using Neural Network With GLCM dan LBP Feature Extraction,” 2024.

V. Chandra And J. H. Jaman, “Identifikasi Varietas Jagung Mutiara berdasarkan Data Citra Digital menggunakan Algoritma K-Nearest Neighbor,” Vol. 16, 2022, [Online]. Available: Https://Journal.Uniku.Ac.Id/Index.Php/Ilkom

C. Jatmoko And D. Sinaga, “A Classification of Batik Lasem using Texture Feature Ecxtraction based on K-Nearest Neighbor,” J. Appl. Intell. Syst. Vol. 3, No. 2, Pp. 96–107, 2018.

T. Harlina, E. Handayani, And M. Informatika, “Klasifikasi Motif Batik Banyuwangi menggunakan Metode K-Nearest Neighbor (K-NN) Berbasis Android,” Mar. 2022.

Y. Kusumawati, A. Susanto, I. U. W. Mulyono, And D. P. Prabowo, “Klasifikasi Batik Kudus berdasarkan Pola menggunakan K-NN dan GLCM,” In Proc. Semin. Nas. Lppm Ump, 2021, Pp. 509–514.

A. E. Minarno, M. Y. Hasanuddin, And Y. Azhar, “Batik Images Retrieval using Pre-Trained Model and K-Nearest Neighbor,” Joiv: Int.J.Inform. Vis., Vol. 7, No. 1, Pp. 115–121, 2023.




DOI: https://doi.org/10.32520/stmsi.v14i2.5008

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