GEOMETRI SKARAL TENUN KODI: MOTIF BELAH KETUPAT, SIMETRI DAN AI SEBAGAI PELESTARIAN LINGKUNGAN
Keywords:
Etnomatematika, Tenun Kodi, Convolutional Neural Network, Pelestarian Lingkungan, GeometriAbstract
Penelitian ini bertujuan untuk mengidentifikasi konsep geometri pada motif belah ketupat tenun Suku Kodi, mengintegrasikannya dengan teknologi Kecerdasan Buatan (AI) melalui arsitektur Convolutional Neural Network (CNN), serta menghasilkan model pembelajaran dan desain kriya ramah lingkungan berbasis data budaya. Penelitian menggunakan pendekatan mixed methods dengan desain sequential explanatory, menggabungkan metode etnografi untuk menggali makna filosofis dan konsep matematika, dengan metode eksperimen untuk menguji akurasi model CNN (ResNet, VGG16, dan EfficientNet) dalam mengklasifikasikan motif tenun Kodi. Hasil penelitian menunjukkan bahwa motif belah ketupat (woloremba) mengandung konsep geometri yang kaya meliputi sisi sejajar, diagonal tegak lurus, dan simetri lipat, dengan makna filosofis sebagai simbol isi rumah, aktivitas keluarga, dan ketidaksempurnaan manusia. Model CNN mencapai akurasi klasifikasi sebesar 91,2%, membuktikan efektivitas AI dalam pengenalan pola tekstil tradisional. Integrasi etnomatematika dan AI membuka peluang pelestarian lingkungan melalui digitalisasi motif, visualisasi 3D, dan pengembangan modul pembelajaran berbasis budaya yang kontekstual tanpa mengeksploitasi sumber daya alam secara berlebihan. Penelitian ini berkontribusi pada pengayaan khazanah etnomatematika Indonesia sekaligus mendukung upaya pelestarian budaya dan lingkungan secara berkelanjutan.
References
Bishop, A. J. (1991). Mathematical Enculturation: A Cultural Perspective on Mathematics Education. Dordrecht: Kluwer Academic Publishers.
Campos, H. D., & Caya, M. V. C. (2024). Utilizing convolutional neural networks for the classification and preservation of Kalinga textile patterns. International Journal of Advanced and Applied Sciences, 11(5).
D'Ambrosio, U. (2001). Etnomatematika: Suatu Pandangan. Yogyakarta: Pustaka Belajar.
Das, A., Deka, A., Medhi, K., & Saikia, M. J. (2024). Deep Learning to Authenticate Traditional Handloom Textile. Information (Switzerland), 15(8). https://doi.org/10.3390/info15080465
Hussain, M. A. I., Khan, B., Wang, Z., & Ding, S. (2020). Woven fabric pattern recognition and classification based on deep convolutional neural networks. Electronics (Switzerland), 9(6). https://doi.org/10.3390/electronics9061048
Kalli, Y. N., & Napfiah, S. (2024). Identifikasi etnomatematika pada motif kain tenun Sumba Barat Daya. Cakrawala: Jurnal Pendidikan, 8(2), 145-155. https://doi.org/10.28989/cakrawala.v1i2.2555
Kamid, A. O., & Antonio, E. O. (2025). Automated Pattern Recognition of Inaul Traditional Fabrics Using Convolutional Neural Networks. In 2025 IEEE 8th International Conference on Electrical, Control and Computer Engineering (InECCE) (pp. 633-637). IEEE. https://doi.org/10.1109/InECCE64959.2025.11151015
Khalim, N. H., Phipps, M. E., & Lee, H. C. (2026). Phylogeny of cultural heritage in Southeast Asia: A computational analysis of artefact evolution. In HCI International 2025 - Late Breaking Papers (pp. 337-360). Springer.
Pasha, Q., Akram, N., Rafique, A., Akber, A., & Abbasi, M. (2024). An Automated Model for Fabric Weave Pattern Recognition and Classification Using Deep Learning Based Image Processing. 2024 Global Conference on Wireless and Optical Technologies (GCWOT), 1-5.
Setiawi, A. P., Edwin, E., & Mau, S. D. I. (2025). Hubungan Filsafat, Kebudayaan, dan Pendidikan Matematika untuk Sekolah Dasar di Sumba Barat Daya, NTT. Jurnal Education and Development, 13(1), 311-318.
Setiawi, A. P., Sabawaly, D. R., Ina, O. T., & Ratu, H. H. (2025). Penguatan Etnomatematika Marapu Melalui PBL dan Teknologi Digital Dalam Pembelajaran Matematika SD di Sumba Barat Daya. Journal of Innovative and Creativity (Joecy), 5(2), 18281-18290. https://doi.org/10.31004/joecy.v5i2.2273
Setiawi, A. P., Zaghu, M. B., & Patty, E. N. S. (2025). Integrating Marapu Indigenous Philosophy and Digital Innovation into the Mathematics Curriculum for Junior High Schools in Southwest Sumba. PPSDP International Journal of Education, 4(2), 944-961. https://doi.org/10.59175/pijed.v4i2.800
Yuan, W., Wang, M., & Huangfu, X. (2026). A sustainability framework and application pathway for AI-assisted restoration and iterative interaction of MR digital heritage based on stakeholders. Scientific Reports. https://doi.org/10.1038/s41598-026-54543-8
Eksplorasi Unsur Matematika dalam Tikar Adat di Kecamatan Loura, Kabupaten Sumba Barat Daya. (2026). Indo-MathEdu Intellectuals Journal, 7(1), 222-227.
Indian Heritage Textile Pattern Recognition Using Deep Convolutional Architectures. (2026). IEEE Xplore.
Al-Majed, R., & Hussain, M. (2024). Entropy-Based Ensemble of Convolutional Neural Networks for Clothes Texture Pattern Recognition. Applied Sciences, 14(22), 10730. https://doi.org/10.3390/app142210730
Campos, H. D., & Caya, M. V. C. (2024). Utilizing convolutional neural networks for the classification and preservation of Kalinga textile patterns. International Journal of Advanced and Applied Sciences, 11(5).
Creswell, J. W., & Clark, V. L. P. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Thousand Oaks, CA: Sage Publications.
Das, A., Deka, A., Medhi, K., & Saikia, M. J. (2024). Deep Learning to Authenticate Traditional Handloom Textile. Information (Switzerland), 15(8). https://doi.org/10.3390/info15080465
Hussain, M. A. I., Khan, B., Wang, Z., & Ding, S. (2020). Woven fabric pattern recognition and classification based on deep convolutional neural networks. Electronics (Switzerland), 9(6). https://doi.org/10.3390/electronics9061048
Kalli, Y. N., & Napfiah, S. (2024). Identifikasi etnomatematika pada motif kain tenun Sumba Barat Daya. Cakrawala: Jurnal Pendidikan, 8(2), 145-155. https://doi.org/10.28989/cakrawala.v1i2.2555
Miles, M. B., & Huberman, A. M. (1994). Qualitative Data Analysis: An Expanded Sourcebook (2nd ed.). Thousand Oaks, CA: Sage Publications.
Setiawi, A. P., Edwin, E., & Mau, S. D. I. (2025). Hubungan Filsafat, Kebudayaan, dan Pendidikan Matematika untuk Sekolah Dasar di Sumba Barat Daya, NTT. Jurnal Education and Development, 13(1), 311-318.
Setiawi, A. P., Sabawaly, D. R., Ina, O. T., & Ratu, H. H. (2025). Penguatan Etnomatematika Marapu Melalui PBL dan Teknologi Digital Dalam Pembelajaran Matematika SD di Sumba Barat Daya. Journal of Innovative and Creativity (Joecy), 5(2), 18281-18290. https://doi.org/10.31004/joecy.v5i2.2273
Setiawi, A. P., Zaghu, M. B., & Patty, E. N. S. (2025). Integrating Marapu Indigenous Philosophy and Digital Innovation into the Mathematics Curriculum for Junior High Schools in Southwest Sumba. PPSDP International Journal of Education, 4(2), 944-961. https://doi.org/10.59175/pijed.v4i2.800
Spradley, J. P. (2016). The Ethnographic Interview. Long Grove, IL: Waveland Press.
Sugiyono. (2019). Metode Penelitian Kuantitatif, Kualitatif, dan R&D. Bandung: Alfabeta.
Wibowo, S., Ambarwati, U., & Firdaus, F. M. (2023). Mathematics Ethnic Learning to Increase Elementary School Students' Understanding of Mathematical Concepts and Patriotism. International Journal of Elementary Education, 7(4), 677-688. (URL tidak ditemukan: 404)
Indian Heritage Textile Pattern Recognition Using Deep Convolutional Architectures. (2026). IEEE Xplore.
Peningkatan Kemampuan Berpikir Komputasional melalui Integrasi Etnomatematika Motif Tenun Sasambo dan Deep Learning. (2025). Garuda Kemdikbud.
D'Ambrosio, U. (2001). Etnomatematika: Suatu Pandangan. Yogyakarta: Pustaka Belajar.
Hussain, M. A. I., Khan, B., Wang, Z., & Ding, S. (2020). Woven fabric pattern recognition and classification based on deep convolutional neural networks. Electronics (Switzerland), 9(6). https://doi.org/10.3390/electronics9061048
Kalli, Y. N., & Napfiah, S. (2024). Identifikasi etnomatematika pada motif kain tenun Sumba Barat Daya. Cakrawala: Jurnal Pendidikan, 8(2), 145-155.
Kamid, A. O., & Antonio, E. O. (2025). Automated Pattern Recognition of Inaul Traditional Fabrics Using Convolutional Neural Networks. In 2025 IEEE 8th International Conference on Electrical, Control and Computer Engineering (InECCE) (pp. 633-637). IEEE.
Khalim, N. H., Phipps, M. E., & Lee, H. C. (2026). Phylogeny of cultural heritage in Southeast Asia: A computational analysis of artefact evolution. In HCI International 2025 - Late Breaking Papers (pp. 337-360). Springer.
Kusno, K., Yolanda, G., & Supiyati, S. (2024). Exploration of Unggan weaving in Minang culture: An ethnomathematics study. Jurnal Elemen, 10(1), 121-134. https://doi.org/10.29408/jel.v10i1.23950
Muhartini, S., Sunyoto, A., & Muhammad, A. H. (2024). Implementation of the CNN Deep Learning Method in Tajong (Sarung) Samarinda Classification. Journal of Applied Sciences, Management and Engineering Technology, 5(2). https://doi.org/10.31284/j.jasmet.2024.v5i2.6406
Putra, M. T. D., & Kusuma, G. P. (2019). Batik Classification using Deep Learning. International Journal of Recent Technology and Engineering (IJRTE), 8(4), 11416-11421. https://doi.org/10.35940/ijrte.d9039.118419
Sari, A., Putri, R. I. I., Zulkardi, & Prahmana, R. C. I. (2024). Ethnomathematics in Indonesian Woven Fabric: The Promising Context in Learning Geometry. Mathematics Teaching Research Journal, 16(5), 157-185.
Septiarini, A., Saputra, R., Tedjawati, A., Wati, M., & Hamdani, H. (2022). Pattern Recognition of Sarong Fabric Using Machine Learning Approach Based on Computer Vision for Cultural Preservation. International Journal of Intelligent Engineering and Systems, 15(5), 284-295. https://doi.org/10.22266/ijies2022.1031.26
Setiawi, A. P., Sabawaly, D. R., Ina, O. T., & Ratu, H. H. (2025). Penguatan Etnomatematika Marapu Melalui PBL dan Teknologi Digital Dalam Pembelajaran Matematika SD di Sumba Barat Daya. Journal of Innovative and Creativity (Joecy), 5(2), 18281-18290. https://doi.org/10.31004/joecy.v5i2.2273
Setiawi, A. P., Zaghu, M. B., & Patty, E. N. S. (2025). Integrating Marapu Indigenous Philosophy and Digital Innovation into the Mathematics Curriculum for Junior High Schools in Southwest Sumba. PPSDP International Journal of Education, 4(2), 944-961. https://doi.org/10.59175/pijed.v4i2.800
Wibowo, S., Ambarwati, U., & Firdaus, F. M. (2023). Mathematics Ethnic Learning to Increase Elementary School Students' Understanding of Mathematical Concepts and Patriotism. International Journal of Elementary Education, 7(4), 677-688.
Yu, T., et al. (2022). Artificial intelligence for Dunhuang cultural heritage protection: The project and the dataset. International Journal of Computer Vision, 130, 2646-2673.
Yuan, W., Wang, M., & Huangfu, X. (2026). A sustainability framework and application pathway for AI-assisted restoration and iterative interaction of MR digital heritage based on stakeholders. Scientific Reports. https://doi.org/10.1038/s41598-026-54543-8
