DETEKSI MOTIF DAN WARNA DOMINAN BATIK MENGGUNAKAN YOLOv8

Authors

  • Virginia Abuk Klau Universitas Nusantara PGRI Kediri Indonesia
  • Daniel Swanjaya Universitas Nusantara PGRI Kediri Indonesia
  • S. Kom. M. Kom Universitas Nusantara PGRI Kediri Indonesia

DOI:

https://doi.org/10.29407/ab4ksf85

Abstract

Abstrak— Batik merupakan warisan budaya Indonesia yang memiliki keragaman motif dan warna yang tinggi. Proses identifikasi motif dan penentuan warna dominan masih dilakukan secara manual sehingga bersifat subjektif dan kurang efisien. Penelitian ini bertujuan mengembangkan sistem otomatis untuk mendeteksi motif batik dan mengekstraksi warna dominan secara objektif. Metode yang digunakan adalah YOLOv8 untuk deteksi motif batik dan K-Means Clustering untuk ekstraksi warna dominan. Data berupa citra batik diperoleh dari pengambilan langsung dan sumber daring. Tahapan penelitian meliputi pengumpulan data, pra-pemrosesan, pelatihan model, serta analisis warna pada area motif yang terdeteksi. Penelitian ini diharapkan menghasilkan sistem yang mampu meningkatkan akurasi dan efisiensi identifikasi batik serta mendukung digitalisasi budaya.

Keywords:

batik, computer vision, K-Means, warna dominan, YOLOv8

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References

[1] I. A. U. Agustin and E. Latifah, “Upaya Melestarikan Kearifan Lokal Batik Kontemporer Di

Desa Sendang Lamongan,” J. Multidisipliner Bharasumba, vol. 4, no. 1, pp. 75–89, 2025,

doi: 10.62668/bharasumba.v4i1.1346.

[2] M. Hussain, “YOLO-v1 to YOLO-v8, the rise of YOLO and its complementary nature

toward digital manufacturing and industrial defect detection,” Machines, vol. 11, no. 7, p.

677, 2023, doi: 10.3390/machines11070677.

[3] A. Vijayakumar and S. Vairavasundaram, “Yolo-based object detection models: A review

and its applications,” Multimed. Tools Appl., vol. 83, no. 35, pp. 83535–83574, 2024, doi:

10.1007/s11042-024-18872-y.

[4] R. H. Zain, “Metode Ekstraksi Fitur Canny, GLCM dan Segmentasi Warna Menggunakan

K-Means Clustering Dalam Peningkatan Motif Batik,” Indones. J. Comput. Sci., vol. 13, no.

6, 2024, doi: 10.33022/ijcs.v13i6.4514.

[5] M. S. A. Karim, A. A. Hakim, C. S. Rafika, and E. Y. Puspaningrum, “Klasifikasi Motif

Batik Yogyakarta dan Pekalongan Menggunakan Metode GLCM dan CNN Berbasis

Arsitektur EfficientNet,” in Prosiding Seminar Nasional Informatika Bela Negara

(SANTIKA), 2025, pp. 350–356. doi: 10.33005/santika.v5i1.747.

[6] A. R. Dani and I. Handayani, “Klasifikasi motif batik Yogyakarta menggunakan metode

GLCM dan CNN,” J. Teknol. Terpadu, vol. 10, no. 2, pp. 142–156, 2024, doi:

10.54914/jtt.v10i2.1451.

[7] R. A. Nalendra, M. R. D. Fisena, and I. Herdianto, “Klasifikasi Citra Digital Batik Jawa

Berdasarkan Motif dan Warna Menggunakan Metode Convolutional Neural Network,” in

Prosiding Seminar Nasional Informatika Bela Negara (SANTIKA), 2025, pp. 88–92. doi:

10.33005/santika.v5i1.545.

[8] S. Pramudhita, D. Maharani, and E. Y. Puspaningrum, “Segmentasi Warna Batik Pesisir

Jawa Menggunakan HSV dan Average Linkage pada Hierarchical Clustering: Analisis

Visual Warna Dominan pada Motif Batik Pesisir,” in Prosiding Seminar Nasional

Informatika Bela Negara (SANTIKA), 2025, pp. 60–65. doi: 10.33005/santika.v5i1.737.

[9] M. Mao and M. Hong, “YOLO object detection for real-time fabric defect inspection in the

textile industry: A review of YOLOv1 to YOLOv11,” Sensors, vol. 25, no. 7, p. 2270, 2025,

doi: 10.3390/s25072270.

[10] G. M. A. Siregar and N. Novianda, “Visualisasi Serta Efektifitas Colour Clustering dengan

K-Means dan Bee Algorithm,” J. Comput. Inf. Syst., vol. 8, no. 1, pp. 13–30, 2025, doi:

10.31605/jcis.v8i1.5040.

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Published

2026-07-20

Issue

Vol. 10 No. 3 (2026): Prosiding Seminar Nasional Inovasi Teknologi Tahun 2026

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How to Cite

DETEKSI MOTIF DAN WARNA DOMINAN BATIK MENGGUNAKAN YOLOv8. (2026). Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi), 10(3), 2506-2515. https://doi.org/10.29407/ab4ksf85