Indonesian Shadow Puppet Recognition Using VGG-16 and Cosine Similarity

 (*)Ida Bagus Kresna Sudiatmika Mail (STMIK Primakara, Denpasar, Indonesia)
 I Gusti Ayu Agung Sari Dewi (STMIK Primakara, Denpasar, Indonesia)

(*) Corresponding Author

Submitted: November 16, 2020; Published: March 29, 2021


Wayang is one of Indonesia's cultural heritages that must be preserved, where puppets have various functions not only as a means of art, Wayang is also used as a means of religious ceremonies in Indonesia. Therefore, It is very important to preserve the wayang tradition, to preserve it and it is well known by the public, especially young people. In this research, to participate in the preservation of wayang, the authors propose a preservation approach using artificial intelligence and web scraping techniques. Artificial intelligence in this case is carrying out the process of recognizing wayang images and finding information from the puppet data. In searching for information, web scraping techniques are used where information is automatically retrieved from the internet. Information obtained from the internet will be processed using the TF-IDF method and cosine similarity. This study uses the CNN architecture on the existing model, namely VGG-16. The training process is carried out with 200 iterations with an accuracy of 89%. The application of the CNN network architecture and web scraping has been successfully applied and will still be developed in the future to obtain maximum results


Wayang; Convolutional Neural Network; Web Scraping; TF-IDF; Cosine Similarity

Full Text:


Article Metrics

Abstract view : 532 times
PDF - 201 times


K. A. Nugraha et al., “Algoritma Backpropagation Pada Jaringan Saraf Tiruan Untuk Pengenalan Pola Wayang Kulit,” Semin. Nas. Inform., vol. 2013, no. semnasIF, pp. 8–13, 2013, [Online]. Available: Wayang Kulit, Jaringan Saraf Tiruan, Backpropagation, Deteksi Tepi.

M. Muhathir, M. H. Santoso, and D. A. Larasati, “Wayang Image Classification Using SVM Method and GLCM Feature Extraction,” JITE, vol. 4, no. 2, pp. 373–382, Jan. 2021, doi: 10.31289/jite.v4i2.4524.

A. Waleed Salehi, P. Baglat, and G. Gupta, “Review on machine and deep learning models for the detection and prediction of Coronavirus,” Materials Today: Proceedings, Jun. 2020, doi: 10.1016/j.matpr.2020.06.245.

V. Chandrasekar, V. Sureshkumar, T. S. Kumar, and S. Shanmugapriya, “Disease prediction based on micro array classification using deep learning techniques,” Microprocessors and Microsystems, vol. 77, p. 103189, Sep. 2020, doi: 10.1016/j.micpro.2020.103189.

D. Karimi, H. Dou, S. K. Warfield, and A. Gholipour, “Deep learning with noisy labels: Exploring techniques and remedies in medical image analysis,” Medical Image Analysis, vol. 65, p. 101759, Oct. 2020, doi: 10.1016/

S. Han, C. Seng, S. Joseph, and P. Remagnino, “How deep learning extracts and learns leaf features for plant classification,” vol. 71, pp. 1–13, 2017, doi: 10.1016/j.patcog.2017.05.015.

X. Wu et al., “Deep learning-based multi-view fusion model for screening 2019 novel coronavirus pneumonia: A multicentre study,” European Journal of Radiology, vol. 128, p. 109041, Jul. 2020, doi: 10.1016/j.ejrad.2020.109041

Z. Yang, J. Yue, Z. Li, and L. Zhu, “Vegetable Image Retrieval with Fine-tuning VGG Model and Image Hash,” IFAC-PapersOnLine, vol. 51, no. 17, pp. 280–285, 2018.

M. V. Valueva, N. N. Nagornov, P. A. Lyakhov, G. V. Valuev, and N. I. Chervyakov, “Application of the residue number system to reduce hardware costs of the convolutional neural network implementation,” Mathematics and Computers in Simulation, vol. 177, pp. 232–243, Nov. 2020, doi: 10.1016/j.matcom.2020.04.031.

T. Shanthi, R. S. Sabeenian, and R. Anand, “Automatic diagnosis of skin diseases using convolution neural network,” Microprocessors and Microsystems, vol. 76, p. 103074, Jul. 2020

X. Hao, G. Zhang, and S. Ma, “Deep Learning,” International Journal of Semantic Computing, vol. 10, no. 3, pp. 417–439, Sep. 2016, doi: 10.1142/s1793351x16500045

N.J. Majaj and D. G. Pelli, “Deep learning—Using machine learning to study biological vision,” Journal of Vision, vol. 18, no. 13, p. 2, Dec. 2018, doi: 10.1167/18.13.2.

S. Rahman, L. Wang, C. Sun, and L. Zhou, “Deep Learning Based HEp-2 Image Classification: A Comprehensive Review,” Medical Image Analysis, p. 101764, Jul. 2020, doi: 10.1016/ Rahman, L. Wang, C. Sun, and L. Zhou, “Deep Learning Based HEp-2 Image Classification: A Comprehensive Review,” Medical Image Analysis, p. 101764, Jul. 2020, doi: 10.1016/

K. Shankar, A. R. W. Sait, D. Gupta, S. K. Lakshmanaprabu, A. Khanna, and H. M. Pandey, “Automated detection and classification of fundus diabetic retinopathy images using synergic deep learning model,” Pattern Recognition Letters, vol. 133, pp. 210–216, May 2020, doi: 10.1016/j.patrec.2020.02.026.

F. Ertam, “An effective gender recognition approach using voice data via deeper LSTM networks,” Applied Acoustics, vol. 156, pp. 351–358, Dec. 2019, doi: 10.1016/j.apacoust.2019.07.033.

F. Polidoro, R. Giannini, R. L. Conte, S. Mosca, and F. Rossetti, “Web scraping techniques to collect data on consumer electronics and airfares for Italian HICP compilation,” SJI, vol. 31, no. 2, pp. 165–176, May 2015.

B. Zhao, “Web Scraping,” in Encyclopedia of Big Data, Springer International Publishing, 2017, pp. 1–3.

Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Indonesian Shadow Puppet Recognition Using VGG-16 and Cosine Similarity


  • There are currently no refbacks.

Copyright (c) 2021 Ida Bagus Kresna Sudiatmika, I Gusti Ayu Agung Sari Dewi

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

The IJICS (International Journal of Informatics and Computer Science)
Published by STMIK Budi Darma.
Jl. Sisingamangaraja No.338 Simpang Limun, Medan, North Sumatera

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.