Basis Pengetahuan Nilai-nilai Kain Tenun Sumba dengan Model Seci dan Convolutional Neural Network
DOI:
https://doi.org/10.24246/aiti.v20i1.1-15Keywords:
Model Seci, Convolutional Neural Network, websiteAbstract
This study aims to make tacit knowledge explicit about the philosophical values contained in each pattern of Sumba woven fabrics. Loli woven, white textile, Lambaleko fabric from Lamboya, Panggiling textile from Wanokaka and Lamboya, and modification Panggiling from Wanokaka are types of cloth in West Sumba Regency, especially in four sub-districts. This study detects the type of fabric from each existing motif. This study uses the Seci model combined with CNN and is presented on the website. From this research, the tacit knowledge of Sumba woven fabrics can be made explicit. The results from CNN show that of the total epochs built, the accuracy value at the 10th epoch is 80%, the accuracy validation is 100%, the accuracy error value is 0.2638% loss, and 0.0477% validation loss. The high accuracy validation results, low accuracy errors, and low validation loss are caused by the image data used as test data, which is a type of motif with distinctive characteristics, in contrast to image data of plants and other living things, which have various types. Website development as a user interface has been going well, as evidenced by the success of predicting tacit knowledge for each image used as test data.
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S. Astuti, “Eksplorasi Etnomatematika Kain Ulos Batak Toba Untuk Mengungkap Nilai Filosofi Konsep Matematika,” J. MathEducation Nusant., vol. 2, no. 1, pp. 45–50, 2019.
H. Hasbullah, W. Wilaela, and R. Syafitri, “Menenun Bagi Perempuan Melayu Riau: Antara Peluang Usaha dan Pelestarian Budaya,” PALASTREN J. Stud. Gend., vol. 13, no. 1, pp. 195–218, 2020.
B. Susanto, B. Valgian, G. Virginia, and U. Proboyekti, “Perancangan awal model pengetahuan batik indonesia berbasis semantic web,” in Seminar Nasional Teknologi Informasi dan Komunikasi (SEMNASTIK), 2018, vol. 1, no. 1, pp. 424–433.
E. Sediyono, T. Mahatma, and I. Arianti Hunga, “Knowledge Management of Traditional Batik in Central Java,” Int. J. Eng. Technol., vol. 7, no. 4.44, p. 143, 2018, doi: 10.14419/ijet.v7i4.44.26970.
D. Nurcahyanti, “Local Wisdom in Traditional Batik Design,” … Conf. Creat. Ind. 2018 (5th …, vol. 197, pp. 472–478, 2019, [Online]. Available: https://www.atlantis-press.com/proceedings/bcm-18/125910971.
M. Ramprasath, M. V. Anand, and S. Hariharan, “Image classification using convolutional neural networks,” Int. J. Pure Appl. Math., vol. 119, no. 17, pp. 1307–1319, 2018.
E. Sediyono, T. Mahatma, and A. I. R. Hunga, “Strengthening Batik Home-workers through ICT based Fair Trade,” INTERNETWORKING Indones., vol. 8, no. 2, pp. 45–49, 2016.
N. Sharma, V. Jain, and A. Mishra, “An analysis of convolutional neural networks for image classification,” Procedia Comput. Sci., vol. 132, pp. 377–384, 2018.
R. Hidayat, “Knowledge Conversion Untuk Standardisasi Proses Pengecapan Pada Batik Madura,” J. Teknol., vol. 9, no. 1, pp. 21–30, 2017.
F. Sultana, A. Sufian, and P. Dutta, “Advancements in image classification using convolutional neural network,” in 2018 Fourth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), 2018, pp. 122–129.
A. Asriani, M. Dinar, M. Hasan, M. I. Said, and I. Inanna, “TRANSFER PENGETAHUAN DALAM PERSPEKTIF PENDIDIKAN EKONOMI INFORMAL: REVITALISASI NILAI DAN MOTIF KAIN SUTERA DI KABUPATEN WAJO DALAM ERA FASHION MODERN,” Soedirman Econ. Educ. J., vol. 3, no. 2, pp. 58–70, 2021.
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