Pengenalan Citra Kain Tenun Nusa Tenggara Timur Menggunakan SqueezNet dan Decision Tree
DOI:
https://doi.org/10.24002/konstelasi.v4i1.9220Keywords:
pengenalan citra kain tenun, decision tree, Nusa Tenggara Timur , identifikasi motif dan pola, pelestarian budayaAbstract
Pengenalan kain tenun melalui pengolahan citra memiliki nilai tinggi dalam melestarikan warisan budaya dan membantu dalam identifikasi dan klasifikasi produk tekstil tradisional. Untuk tujuan ini, penelitian ini mengusulkan pendekatan yang memanfaatkan pohon keputusan atau Decision Tree untuk mengenali gambar kain tenun khas Nusa Tenggara Timur. Efektivitas dalam klasifikasi data dimensi tinggi menjadikannya alat yang ideal untuk memodelkan pola unik yang ada dalam gambar kain tenun. Data set penelitian ini terdiri dari berbagai jenis kain tenun Nusa Tenggara Timur, dan hasil eksperimen menunjukkan keakuratan pendekatan yang kami usulkan dalam mengenali kain-kain ini, mencapai tingkat keberhasilan yang menjanjikan dalam klasifikasi motif dan pola yang kompleks. Temuan ini merupakan kontribusi yang signifikan terhadap pengembangan sistem otomatis untuk mengidentifikasi dan mendokumentasikan kain tenun tradisional, yang pada gilirannya dapat mendukung inisiatif pelestarian budaya dan pertumbuhan industri lokal di Nusa Tenggara Timur.
The recognition of woven fabrics through image processing has a high value in preserving cultural heritage and assisting in the identification and classification of traditional textile products. To this end, this study proposes an approach that utilizes a Decision Tree (DT) to recognize images of woven fabrics typical of East Nusa Tenggara. DT's effectiveness in high-dimensional data classification makes it an ideal tool for modeling unique patterns present in woven fabric drawings. The dataset consisted of different types of East Nusa Tenggara woven fabrics, and experimental results showed the accuracy of the approach in recognizing these fabrics, achieving promising success rates in the classification of complex motifs and patterns. These findings represent a significant contribution to the development of automated systems for identifying and documenting traditional woven fabrics, which in turn can support cultural preservation initiatives and local industrial growth in East Nusa Tenggara.
References
Y. P. K. Kelen and B. Baso, “Klasifikasi Tenun Timor Menggunakan Metode SVM Berdasarkan Speeded Up Robust Features,” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 10, no. 6, Art. no. 6, Dec. 2023, doi: 10.25126/jtiik.1067625.
S. Ulfa, T. E. br Sinulingga, and J. Sinulingga, “Kain Tenun Tradisional: Warisan Budaya dan Industri Kreatif,” Jurnal Pendidikan Tambusai, vol. 7, no. 3, pp. 29709–29715, Dec. 2023, doi: 10.31004/jptam.v7i3.11780.
O. R. Sutopo, E. Z. L. Astuti, M. P. Kuslarassakti, M. Nurrizky, R. Satriani, and A. DwiPrakoso, “Problematika Regenerasi dalam Praktik Budaya Tenun Lokal di Sejatidesa Yogyakarta,” vol. 17, no. 2, 2023.
N. Insani et al., “Pelestarian Budaya Tenun Ikat Bandar Kidul Melalui Kegiatan Kids Fashion Show Competition,” satwika, vol. 2, no. 2, pp. 115–120, Dec. 2022, doi: 10.21009/satwika.020206.
Guslinda, “Kerajinan Tenun Songket Melayu Riau Untuk Pelestarian Kearifan Lokal,” Jurnal Pigur, vol. 2, no. 1, pp. 124–130, 2017.
N. L. Maulidiyah, “Motif Khas Tenun Ikat Troso Sebagai Sumber Pembelajaran Muatan Lokal Seni Rupa Smp Di Kabupaten Jepara,” Eduarts Jurnal Pendidikan Seni, vol. 12, no. 1, pp. 70–83, 2023.
S. Wening and P. D. A. Kusumadewi, “Tren Berkain Generasi Z: Peluang Pengembangan Industri Kreatif Bidang Busana”, [Online]. Available: https://journal.uny.ac.id/index.php/ptbb/article/viewFile/68011/20550
R. Aprianti, K. Evandari, R. A. Pramunendar, and Moch. Soeleman, “Comparison Of Classification Method On Lombok Songket Woven Fabric Based On Histogram Feature,” in 2021 International Seminar on Application for Technology of Information and Communication (iSemantic), Sep. 2021, pp. 196–200. doi: 10.1109/iSemantic52711.2021.9573223.
K. S. Charan, SP. Chokkalingam, and K. K. Shantha Sundari, “Efficiency of Decision tree algorithm for Brain Tumor MRI Images comparing with SVM Algorithm,” in 2022 14th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS), Nov. 2022, pp. 1–4. doi: 10.1109/MACS56771.2022.10022493.
T. Shafira and A. Fauzy, “Implementasi Image Classification Menggunakan MetodeConvolutional Neural Network (CNN) Pada Citra KainTenun,” UII, Jakarta, 2017.
P. A. Wicaksana, I. M. Sudarma, and D. C. Khrisne, “Pengenalan Pola Motif Kain Tenun Gringsing Menggunakan Metode Convolutional Neural Network Dengan Model Arsitektur Alexnet,” Jurnal SPEKTRUM, vol. 6, no. 3, pp. 159–168, Sep. 2019, doi: 10.24843/SPEKTRUM.2019.v06.i03.p23.
Y. Rizki, R. M. Taufiq, H. Mukhtar, and D. Putri, “Klasifikasi Pola Kain Tenun Melayu Menggunakan Faster R-CNN,” IT Journal Research and Development, vol. 5, no. 2, Art. no. 2, Jan. 2021, doi: 10.25299/itjrd.2021.vol5(2).5831.
M. A. Qureshi, R. A. Butt, M. Z. Arfeen, and N. Akram, “Fully Automatic Weave Identification in Woven Fabrics Using Digital Image Processing,” Engineering Proceedings, vol. 20, no. 1, Art. no. 1, 2022, doi: 10.3390/engproc2022020001.
X. Liang, W. Liu, Y. Zhang, J. Yu, and H. Qu, “Dimensionality-Varied Convolutional Neural Network for Hyperspectral Image Classification With Small-Sized Labeled Samples,” in 2018 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Sep. 2018, pp. 1–5. doi: 10.1109/WHISPERS.2018.8747243.
C. Y. Jerandu et al., “Image Classification of Decapterus Macarellus Using Ridge Regression,” in 2022 8th International Conference on Education and Technology (ICET), Oct. 2022, pp. 81–86. doi: 10.1109/ICET56879.2022.9990820.
A. J. Lado et al., “Comparison of Neural Network and Random Forest Classifier Performance on Dragon Fruit Disease,” in 2021 International Electronics Symposium (IES), Sep. 2021, pp. 287–291. doi: 10.1109/IES53407.2021.9593992.
S. Soebandhi, A. G. Sooai, K. Kristiningsih, and A. Nugroho, “Multisensory Culinary Image Classification based on SqueezeNet and Support Vector Machine,” presented at the The Information Technology International Seminar, Batu Malang: IEEE, Oct. 2023. [Online]. Available: http://2023.itisconf.org/
K. Sanjar, A. Rehman, A. Paul, and K. JeongHong, “Weight Dropout for Preventing Neural Networks from Overfitting,” in 2020 8th International Conference on Orange Technology (ICOT), Dec. 2020, pp. 1–4. doi: 10.1109/ICOT51877.2020.9468799.
R. Kinoyama, E. A. M. Perez, and H. Iba, “Preventing Overfitting of LSTMs using Ant Colony Optimization,” in 2021 10th International Congress on Advanced Applied Informatics (IIAI-AAI), Jul. 2021, pp. 343–350. doi: 10.1109/IIAI-AAI53430.2021.00061.
Z. Biczó, S. Szénási, and I. Felde, “Safe Overfitting of Boosted Tree Algorithm in Heat Transfer Modeling,” in 2022 IEEE 20th Jubilee World Symposium on Applied Machine Intelligence and Informatics (SAMI), Mar. 2022, pp. 000379–000382. doi: 10.1109/SAMI54271.2022.9780808.
L. Alzubaidi et al., “Review of deep learning: concepts, CNN architectures, challenges, applications, future directions,” Journal of Big Data, vol. 8, no. 1, p. 53, Mar. 2021, doi: 10.1186/s40537-021-00444-8.
A. G. Sooai, “Seleksi Fitur untuk Pengenalan Gestur Tangan 3D secara Dinamis pada Interaksi Benda Virtual Menggunakan Hybrid GMM,” doctoral, Institut Teknologi Sepuluh Nopember, Surabaya, 2020. Accessed: Jul. 04, 2023. [Online]. Available: https://repository.its.ac.id/81786/
A. G. Sooai, A. N. Rumaksari, K. Khamid, N. Z. Fanani, S. Sumpeno, and M. H. Purnomo, “Deteksi Gestur Lengan Dinamis pada Lingkungan Virtual Tiga Dimensi Koleksi Warisan Budaya,” Jurnal Nasional Teknik Elektro dan Teknologi Informasi, vol. 7, no. 4, Art. no. 4, Nov. 2018.
A. G. Sooai, K. Yoshimoto, H. Takahashi, S. Sumpeno, and M. H. Purnomo, “Dynamic Hand Gesture Recognition on 3D Virtual Cultural Heritage Ancient Collection Objects Using k-Nearest Neighbor,” 2018.
N. M. R. Mamulak, P. A. Nani, and A. G. Sooai, Deteksi Anomali dan Pembelajaran Mesin, 1st ed. Sleman: Karya Bakti Makmur, 2021.
