Recognition of Toraja Carving Motifs Using Texture Features with GLCM

https://doi.org/10.22146/ijccs.111590

Imelda Imelda(1*), Gunawan Pria Utama(2), Asep Cahyana(3)

(1) Universitas Budi Luhur
(2) Universitas Budi Luhur
(3) Universitas Budi Luhur
(*) Corresponding Author

Abstract


Indonesia comprises a diverse array of ethnic groups and cultures. Each ethnic group has unique carving motifs rich in philosophical meaning. Toraja motifs are among the most distinctive in the world. These motifs are often found in traditional houses, textiles, and architectural ornaments. However, people's understanding of the symbolic value of these carvings remains limited, thereby risking cultural erosion. This study aims to recognize Toraja carving motifs using digital image processing, specifically through the extraction of Gray Level Co-occurrence Matrix (GLCM) texture features, which include contrast, correlation, energy, and homogeneity at 0° orientation. The Toraja carving dataset was processed through preprocessing, feature extraction, and thresholding-based classification stages. This study contributes to the combination of GLCM and thresholding that can improve accuracy while providing a computationally efficient solution for traditional motif pattern recognition. Experimental results show that thresholds of 0.002 and 0.004 produce recognition accuracies of 100% and 82%, respectively.

Keywords


GLCM; threshold; digital image processing; Toraja; carving

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References

H. Lumbantobing, I. Wahyudi, and A. Lamba, “Ethnomathematics Exploration of the Toraja Tribe Tongkonan House ’ s Traditional Carving,” Int. J. Sci. Basic Appl. Res., vol. 68, no. 1, pp. 72–85, 2023. L. W. Sari, “Ethnomathematics in Structure and Carving Patterns of Torajan Traditional House Building,” Ethnomathematics J., vol. 4, no. 2, pp. 132–148, 2023. I. P. Yusuf, A. Asmunandar, and A. Ahmadin, “Traditional Woven Fabric Craftsmen into Barana: A Creative Space Based on the Culture of the North Toraja Community 2017-2023,” TRILOGI J. Ilmu Teknol. Kesehatan, dan Hum., vol. 6, no. 1, pp. 36–42, 2025, doi: 10.33650/trilogi.v6i1.10482. S. Gusvianto, H. Upu, and A. Talib, “Identifiying Traditional House of Toraja Carving toward Geometry Transformation Type,” Adcances Soc. Sci. Educ. Humanit. Res., vol. 611, pp. 522–525, 2021, [Online]. Available: https://www.atlantis-press.com/proceedings/icoesm-21/125965642%0Ahttps://www.atlantis-press.com/article/125965642.pdf. P. Karuru, Y. Para’pak, and T. Kabanga, “Storytelling on Toraja Carving in Geometry Learning,” Atlantis Press, Atlantis Press SARL, pp. 476–483, 2024. doi: 10.2991/978-2-38476-108-1. Y. Yusrijal, L. Aliyan, and R. Rina, “The Impact of Digitalization on Traditional Handicraft Markets Among the Toraja Community,” J. Soc. Civilecial, vol. 3, no. 1, pp. 48–58, 2025, doi: 10.71435/610846. Y. Ba’ru, Arismunandar, and Anshari, “Practical Teaching Materials for Transformational Geometry Based on Visuals Ethnomathematics for SMA Toraja Class IX,” Asian J. Educ. Soc. Stud., vol. 50, no. 5, pp. 531–539, 2024, doi: 10.9734/ajess/2024/v50i51383. N. Wardhani, B. E. W. Asrul, A. R. Tampang, S. Zuhriyah, and A. L. Arda, “Classification of Toraja Wood Carving Motif Images Using Convolutional Neural Network (CNN),” J. RESTI, vol. 8, no. 4, pp. 486–495, 2024, doi: 10.29207/resti.v8i4.5897. Herman et al., “A Comperative Study on Efficacy of CNN VGG-16, DenseNet121, ResNet50V2, And EfficientNetB0 in Toraja Carving Classification,” Indones. J. Data Sci., vol. 6, no. 1, pp. 123–133, 2025, doi: 10.56705/ijodas.v6i1.220. Herman, M. Akbar, H. Nasir, Herdianti, H. Azis, and L. N. Hayati, “Comparative Performance of ResNet Architectures for Toraja Carving Image Classification with Data Augmentation,” J. RESTI, vol. 9, no. 4, pp. 737–744, 2025, doi: 10.29207/resti.v9i4.6181. Y. Azhar and D. R. Akbi, “Performance Comparison of GLCM Features and Preprocessing Effect on Batik Image Retrieval,” Int. J. Informatics Vis., vol. 8, no. 3, pp. 1339–1343, 2024, doi: 10.62527/joiv.8.3.2179. P. N. Andono and E. H. Rachmawanto, “Evaluasi Ekstraksi Fitur GLCM dan LBP Menggunakan Multikernel SVM untuk Klasifikasi Batik,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 1, pp. 1–9, 2021, doi: 10.29207/resti.v5i1.2615. A. C. Siregar and B. C. Octariadi, “Classification of Sambas Traditional Fabric ‘Kain Lunggi’ Using Texture Feature,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 13, no. 4, p. 389, 2019, doi: 10.22146/ijccs.49782. L. Wijayanti, “Identitas Visual Ragam Hias Toraja pada Desain Interior Café Tator,” Institut Kesenian Jakarta, 2011. [Online]. Available: https://repository.ikj.ac.id/476/1/Tesis Pascasarjana IKJ-Identitas Visual Toraja %28Lily W%29-compressed.pdf F. A. Listya and N. Rokhman, “Classification of Tangerine (Citrus Reticulata Blanco) Quality Using Combination of GLCM, HSV, and K-NN,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 13, no. 4, p. 357, 2019, doi: 10.22146/ijccs.47906.



DOI: https://doi.org/10.22146/ijccs.111590

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