Pemanfaatan algoritma Convolutional Neural Network (CNN) untuk klasifikasi jenis noken

Wahyuni Fajrin Rosyidah; Christian Dwi Suhendra; Lion Ferdinand Lion Ferdinand

doi:10.24246/aiti.v23i1.89-105

Authors

Wahyuni Fajrin Rosyidah Jurusan Teknik Informatika, Fakultas Teknik, Universitas Papua https://orcid.org/0009-0007-8016-2999
Christian Dwi Suhendra Jurusan Teknik Informatika, Fakultas Teknik, Universitas Papua
Lion Ferdinand Lion Ferdinand Jurusan Teknik Informatika, Fakultas Teknik, Universitas Papua

DOI:

https://doi.org/10.24246/aiti.v23i1.89-105

Keywords:

noken, Convolutional Neural Network, transfer learning, image classification, Papua culture

Abstract

Noken is a traditional bag from Papua that holds great cultural value and has been recognized by UNESCO as Intangible Cultural Heritage. The diversity of noken types based on motifs, shapes, and regions of origin presents a challenge for the identification process, which is still carried out manually. This study aims to develop an automatic noken image classification system using a Convolutional Neural Network (CNN) with transfer learning. Three CNN architectures used in this study are VGG16, InceptionV3, and MobileNetV2. The dataset consists of 500 noken images, divided into two types: Bitu Agia and Junum Ese. The training process was conducted using the TensorFlow library with the best parameters. Evaluation was carried out using accuracy, precision, recall, and F1-score metrics, as well as graphical visualizations of accuracy and loss. The results showed that MobileNetV2 achieved the best performance with an accuracy of 97 percent, followed by InceptionV3 with 96 percent, and VGG16 with 87 percent. This study demonstrates that the deep learning approach is effective in the image classification of cultural objects and can support the digital preservation of Papuan culture.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

References

UNESCO, “UNESCO - Noken multifunctional knotted or woven bag, handcraft of the people of Papua,” UNESCO Intangible Cultural Heritage. Accessed: Apr. 23, 2025. [Online]. Available: https://ich.unesco.org/en/USL/noken-multifunctional-knotted-or-woven-bag-handcraft-of-the-people-of-papua-00619

A. O. Wanma, R. L. Cabuy, H. F. Z. Peday, and M. Beljai, “Ethnobotanical aspect of Noken: Case study in the High Mountain Indigenous community of Papua Island, Indonesia,” vol. 12, no. 2, 2013.

A. Salhuteru and F. K. Hutubessy, “The Transformation of Noken Papua: Understanding the Dynamics of Noken’s Commodification as the Impact of UNESCO’s Heritage Recognition,” JSW, vol. 4, no. 2, pp. 151–164, Oct. 2020, doi: 10.21580/jsw.2020.4.2.5569. DOI: https://doi.org/10.21580/jsw.2020.4.2.5569

Y. Auwe, S. H. B. Wibowo, and P. G. Prabasmara, “Penerapan simbolisasi noken dan honai pada disain bentuk pusat budaya noken Papua di Nabire,” vol. 6, no. 1, 2023. DOI: https://doi.org/10.37631/pendapa.v6i1.867

L. S. Iliadis, V. Kurkova, and B. Hammer, “Brain-inspired computing and machine learning,” Neural Comput & Applic, vol. 32, no. 11, pp. 6641–6643, Jun. 2020, doi: 10.1007/s00521-020-04888-6. DOI: https://doi.org/10.1007/s00521-020-04888-6

J. Pardede, B. Sitohang, S. Akbar, and M. L. Khodra, “Implementation of Transfer Learning Using VGG16 on Fruit Ripeness Detection,” IJISA, vol. 13, no. 2, pp. 52–61, Apr. 2021, doi: 10.5815/ijisa.2021.02.04. DOI: https://doi.org/10.5815/ijisa.2021.02.04

A. M. Jawa and A. Iriani, “Basis Pengetahuan Nilai-nilai Kain Tenun Sumba dengan Model Seci dan Convolutional Neural Network,” AITI, vol. 20, no. 1, pp. 1–15, Mar. 2023, doi: 10.24246/aiti.v20i1.1-15. DOI: https://doi.org/10.24246/aiti.v20i1.1-15

R. Rianto and D. Risdho Listianto, “Convolutional Neural Network untuk mengklasifikasi tingkat keparahan jerawat,” AITI, vol. 20, no. 2, pp. 167–176, Aug. 2023, doi: 10.24246/aiti.v20i2.167-176. DOI: https://doi.org/10.24246/aiti.v20i2.167-176

J. Gupta, S. Pathak, and G. Kumar, “Deep Learning (CNN) and Transfer Learning: A Review,” J. Phys.: Conf. Ser., vol. 2273, no. 1, p. 012029, May 2022, doi: 10.1088/1742-6596/2273/1/012029. DOI: https://doi.org/10.1088/1742-6596/2273/1/012029

X. Han et al., “Pre-trained models: Past, present and future,” AI Open, vol. 2, pp. 225–250, 2021, doi: 10.1016/j.aiopen.2021.08.002. DOI: https://doi.org/10.1016/j.aiopen.2021.08.002

S. Basu et al., “Efficient Equivariant Transfer Learning from Pretrained Models,” Oct. 10, 2023, arXiv: arXiv:2305.09900. doi: 10.48550/arXiv.2305.09900.

Y. E. H. Maur, A. J. Santoso, and Pranowo, “Fine Tuned of DenseNET121 to Classify NTT Weaving Motifs on Mobile Application,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 13, no. 6, pp. 2156–2163, Dec. 2023, doi: 10.18517/ijaseit.13.6.18314. DOI: https://doi.org/10.18517/ijaseit.13.6.18314

S. Tena and B. Y. Dwiandiyanta, “TRANSFORMING WOVEN IKAT FABRIC: ADVANCED CLASSIFICATION VIA TRANSFER LEARNING AND CONVOLUTIONAL NEURAL NETWORKS,” JME, pp. 73–82, Oct. 2023, doi: 10.35508/jme.v12i2.12579. DOI: https://doi.org/10.35508/jme.v12i2.12579

V. H. Athala, A. Haris Rangkuti, N. F. Luthfi, S. Vikri Aditama, and J. M. Kerta, “Improved pattern recognition of various traditional clothes with Convolutional neural network,” in 2021 3rd International Symposium on Material and Electrical Engineering Conference (ISMEE), Bandung, Indonesia: IEEE, Nov. 2021, pp. 15–20. doi: 10.1109/ISMEE54273.2021.9774136. DOI: https://doi.org/10.1109/ISMEE54273.2021.9774136

M. Utami, E. Ermatita, and A. Abdiansyah, “Transfer Learning Implementation for Batik Besurek Motifs Classification: A Comparative Study,” in 2024 Ninth International Conference on Informatics and Computing (ICIC), Medan, Indonesia: IEEE, Oct. 2024, pp. 1–5. doi: 10.1109/ICIC64337.2024.10957440. DOI: https://doi.org/10.1109/ICIC64337.2024.10957440

L. Fitriani, D. Tresnawati, and M. B. Sukriyansah, “Image Classification On Garutan Batik Using Convolutional Neural Network with Data Augmentation,” JUITA, vol. 11, no. 1, p. 107, May 2023, doi: 10.30595/juita.v11i1.16166. DOI: https://doi.org/10.30595/juita.v11i1.16166

T. Abdullah, K. Saputra S, H. Syahputra, Z. Indra, and D. Kartika, “Comparative Analysis of Model Architectures Using Transfer Learning Approach in Convolutional Neural Networks for Traditional Ulos Fabric Classification,” j. of artif. intell. and eng. appl., vol. 4, no. 2, pp. 628–638, Feb. 2025, doi: 10.59934/jaiea.v4i2.719. DOI: https://doi.org/10.59934/jaiea.v4i2.719

R. Roland, C. Angelica, J. A. Diputra, Z. H. Azizul, and D. Fitrianah, “CNN Classifier Parameter Optimization with Genetic Algorithms: A Case Study of Indonesian Batik Patterns,” Int. J. Comp. Intel. Appl., vol. 23, no. 02, p. 2450004, Jun. 2024, doi: 10.1142/S1469026824500044. DOI: https://doi.org/10.1142/S1469026824500044

R. Y. Rika and R. P. Putra, “Klasifikasi Kain Tenun Adat Malaka Menggunakan Metode Convolutional Neural Network (CNN),” vol. 12, no. 1, 2025. DOI: https://doi.org/10.35957/jatisi.v12i1.9345

E. Khoirunnisa et al., “Enhanced Semarang Batik Classification using MobileNetV2 and Data Augmentation,” SinkrOn, vol. 9, no. 1, pp. 43–54, Jan. 2025, doi: 10.33395/sinkron.v9i1.14308. DOI: https://doi.org/10.33395/sinkron.v9i1.14308

E. Ermatita, H. Noprisson, and A. Abdiansah, “Palembang songket fabric motif image detection with data augmentation based on ResNet using dropout,” Bulletin EEI, vol. 13, no. 3, pp. 1991–1999, Jun. 2024, doi: 10.11591/eei.v13i3.6883. DOI: https://doi.org/10.11591/eei.v13i3.6883

S. S. F. Ardyani and C. A. Sari, “A Web-Based for Demak Batik Classification Using VGG16 Convolutional Neural Network,” ASSET, vol. 6, no. 4, p. 0240406, Aug. 2024, doi: 10.26877/asset.v6i4.771. DOI: https://doi.org/10.26877/asset.v6i4.771

A. Rangkuti, V. Hasbi Athala, N. Luthfi, S. Aditama, and J. Kerta, “Reliable of Traditional Cloth Pattern Classification Using Convolutional Neural Network,” in Proceedings of the 2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS), in IEEE Conferences. Ipoh, Malaysia: IEEE, Sep. 2021, pp. 1–6. doi: 10.1109/AiDAS53897.2021.9574402. DOI: https://doi.org/10.1109/AiDAS53897.2021.9574402

N. Dianda, A. S. Rachman, and M. S. Yadnya, “Identifikasi Citra Motif Kain Tenun Sumbawa (Kre Alang) Menggunakan Metode Convolutional Neural Network Arsitektur MobileNetV2,” vol. 6, no. 2, 2025. DOI: https://doi.org/10.47065/josh.v6i2.6774

K. Maharana, S. Mondal, and B. Nemade, “A review: Data pre-processing and data augmentation techniques,” Global Transitions Proceedings, vol. 3, no. 1, pp. 91–99, Jun. 2022, doi: 10.1016/j.gltp.2022.04.020. DOI: https://doi.org/10.1016/j.gltp.2022.04.020

A. R. Beeravolu, S. Azam, M. Jonkman, B. Shanmugam, K. Kannoorpatti, and A. Anwar, “Preprocessing of Breast Cancer Images to Create Datasets for Deep-CNN,” IEEE Access, vol. 9, pp. 33438–33463, 2021, doi: 10.1109/ACCESS.2021.3058773. DOI: https://doi.org/10.1109/ACCESS.2021.3058773

M. F. Aslan, K. Sabanci, A. Durdu, and M. F. Unlersen, “COVID-19 diagnosis using state-of-the-art CNN architecture features and Bayesian Optimization,” Computers in Biology and Medicine, vol. 142, p. 105244, Mar. 2022, doi: 10.1016/j.compbiomed.2022.105244. DOI: https://doi.org/10.1016/j.compbiomed.2022.105244

C. F. G. D. Santos and J. P. Papa, “Avoiding Overfitting: A Survey on Regularization Methods for Convolutional Neural Networks,” ACM Comput. Surv., vol. 54, no. 10s, pp. 1–25, Jan. 2022, doi: 10.1145/3510413. DOI: https://doi.org/10.1145/3510413

A. H. Md. Linkon, Md. M. Labib, T. Hasan, M. Hossain, and M.-E.- Jannat, “Deep learning in prostate cancer diagnosis and Gleason grading in histopathology images: An extensive study,” Informatics in Medicine Unlocked, vol. 24, p. 100582, 2021, doi: 10.1016/j.imu.2021.100582. DOI: https://doi.org/10.1016/j.imu.2021.100582

X. Zhao, L. Wang, Y. Zhang, X. Han, M. Deveci, and M. Parmar, “A review of convolutional neural networks in computer vision,” Artif Intell Rev, vol. 57, no. 4, p. 99, Mar. 2024, doi: 10.1007/s10462-024-10721-6. DOI: https://doi.org/10.1007/s10462-024-10721-6

M. Ye et al., “A Lightweight Model of VGG-16 for Remote Sensing Image Classification,” IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing, vol. 14, pp. 6916–6922, 2021, doi: 10.1109/JSTARS.2021.3090085. DOI: https://doi.org/10.1109/JSTARS.2021.3090085

C. Szegedy et al., “Going Deeper with Convolutions,” Sep. 17, 2014, arXiv: arXiv:1409.4842. doi: 10.48550/arXiv.1409.4842.

J. Cao, M. Yan, Y. Jia, X. Tian, and Z. Zhang, “Application of a modified Inception-v3 model in the dynasty-based classification of ancient murals,” EURASIP J. Adv. Signal Process., vol. 2021, no. 1, p. 49, Dec. 2021, doi: 10.1186/s13634-021-00740-8. DOI: https://doi.org/10.1186/s13634-021-00740-8

O. Iparraguirre-Villanueva, V. Guevara-Ponce, O. R. Paredes, F. Sierra-Liñan, J. Zapata-Paulini, and M. Cabanillas-Carbonell, “Convolutional Neural Networks with Transfer Learning for Pneumonia Detection,” IJACSA, vol. 13, no. 9, 2022, doi: 10.14569/IJACSA.2022.0130963. DOI: https://doi.org/10.14569/IJACSA.2022.0130963

M. Rathi, A. A. Shah, S. Ali, E. N. Baro, A. W. Memon, and A. Inam, “Binary inception V3 deep learning based image classifier for the detection of breast cancer,” presented at the THIRD INTERNATIONAL CONFERENCE ON KEY ENABLING TECHNOLOGIES (KEYTECH2023), Istanbul, Turkey, 2024, p. 060004. doi: 10.1063/5.0214634. DOI: https://doi.org/10.1063/5.0214634

M. Mujahid, F. Rustam, R. Álvarez, J. Luis Vidal Mazón, I. D. L. T. Díez, and I. Ashraf, “Pneumonia Classification from X-ray Images with Inception-V3 and Convolutional Neural Network,” Diagnostics, vol. 12, no. 5, p. 1280, May 2022, doi: 10.3390/diagnostics12051280. DOI: https://doi.org/10.3390/diagnostics12051280

E. Kristiani, Y.-T. Tsan, P.-Y. Liu, N. Y. Yen, and C.-T. Yang, “Binary and Multi-Class Assessment of Face Mask Classification on Edge AI Using CNN and Transfer Learning,” Human-centric Computing and Information Sciences, vol. 12, no. 0, pp. 729–744, Nov. 2022, doi: 10.22967/HCIS.2022.12.053.

A. Bota, “Computationally Efficient Architectures for Computer Vision,” 2024.

M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT: IEEE, Jun. 2018, pp. 4510–4520. doi: 10.1109/CVPR.2018.00474. DOI: https://doi.org/10.1109/CVPR.2018.00474

R. Indraswari, R. Rokhana, and W. Herulambang, “Melanoma image classification based on MobileNetV2 network,” Procedia Computer Science, vol. 197, pp. 198–207, 2022, doi: 10.1016/j.procs.2021.12.132. DOI: https://doi.org/10.1016/j.procs.2021.12.132

M. C. Irmak, T. Aydin, and M. Yaganoglu, “Monkeypox Skin Lesion Detection with MobileNetV2 and VGGNet Models,” in 2022 Medical Technologies Congress (TIPTEKNO), Antalya, Turkey: IEEE, Oct. 2022, pp. 1–4. doi: 10.1109/TIPTEKNO56568.2022.9960194. DOI: https://doi.org/10.1109/TIPTEKNO56568.2022.9960194

F. J. M. Shamrat, S. Azam, A. Karim, K. Ahmed, F. M. Bui, and F. De Boer, “High-precision multiclass classification of lung disease through customized MobileNetV2 from chest X-ray images,” Computers in Biology and Medicine, vol. 155, p. 106646, Mar. 2023, doi: 10.1016/j.compbiomed.2023.106646. DOI: https://doi.org/10.1016/j.compbiomed.2023.106646

N. Idris, C. F. Mohd Foozy, and P. Shamala, “A Generic Review of Web Technology: DJango and Flask,” Int. J. of Adv. Sci. Comp. and Eng., vol. 2, no. 1, pp. 34–40, May 2021, doi: 10.30630/ijasce.2.3.29. DOI: https://doi.org/10.30630/ijasce.2.3.29