(Universitas Negeri Malang, Indonesia)(2) Wahyu Arbianda Yudha Pratama
(Universitas Negeri Malang, Indonesia)(3) Anik Nur Handayani
(Universitas Negeri Malang, Indonesia)(4) Anusua Ghosh
(ASCEE Australia Section, Australia)*corresponding author
AbstractIndonesia is a country with diverse cultures. One of which is Wayang Kulit, which has been recognized by UNESCO. Wayang kulit has a variety of names and personalities, however most younger generations are not familiar with the characters of these shadow puppets. With today's rapid technological advancements, people could use this technology to detect objects using cameras. Convolutional Neural Network (CNN) is one method that can be used. CNN is a learning process that is included in the Deep Learning section and is used to find the best representation. The CNN is commonly used for object detection, would be used to classify good and bad characters. The data used consists of 100 black and white puppet images that were downloaded one at a time. The data was obtained through a training process that uses the CNN method and Google Colab to help speed up the training process. After that, a new model is created to test the puppet images. The result obtained a 92 percent accuracy rate, means that CNN can differentiate the Wayang Kulit character
KeywordsImage Classification; Wayang Kulit; Character Object; Detection Convolutional Neural Network (CNN)
|
DOIhttps://doi.org/10.31763/viperarts.v3i1.373 |
Article metrics10.31763/viperarts.v3i1.373 Abstract views : 2163 | PDF views : 816 |
Cite |
Full Text Download
|
References
M. Ilhamullah and M. Rachmawati, “Kesinambungan pada Galeri Kesenian Wayang Kulit Jawa Timur,” J. Sains dan Seni ITS, vol. 3, no. 2, pp. G42–G45, 2014.
P. Priyanto, “Menggali Nilai-Nilai Kepemimpinan Budi Luhur Dalam Pertunjukkan Wayang,” J. Nas. Teknol. Terap. Berbas. Kearifan Lokal, pp. 447–452, 2019.
S. Subiyantoro and S. S. Fadhilah, “A Study on Teachers’ Perceptions towards Cultural Arts Subject Using Wayang Kulit Purwa to Students of Junior High School in Solo Raya,” Int. J. Pedagog. Teach. Educ., vol. 4, no. 2, p. 138, Dec. 2020.
Y. L. Pramono, S. Suyanto, and A. Wahida, “Shadow Puppet Arts as The Formation of Young Generation Character,” in Proceeding of International Conference on Art, Language, and Culture, 2017, pp. 397–404.
M. I. Cohen, “Wayang in Jaman Now : Reflexive Traditionalization and Local, National and Global Networks of Javanese Shadow Puppet Theatre,” Theatr. Res. Int., vol. 44, no. 1, pp. 40–57, Mar. 2019.
Y. L. Pramono and A. W. Suyanto, “Learning for Making Wayang Kulit in Sanggar Asta Kenya Art,” Int. J. Adv. Multidiscip. Sci. Res. ISSN 2581-4281, 2 (2), February, 2019,# Art, vol. 1211, pp. 1–12, 2019.
A. Ahmadi, “The Creativity of Wayang Kulit (Shadow Puppet) Crafts in Surakarta,” Arts Des. Stud., vol. 58, pp. 41–51, 2017.
M. E. Varela, “Wayang Hip Hop: Java’s Oldest Performance Tradition Meets Global Youth Culture,” Asian Theatr. J., vol. 31, no. 2, pp. 481–504, 2014.
M. Downes, “Hybridities and Deep Histories in Indonesian Wayang Manga Comics,” Situations, vol. 8, no. 2, p. 5, 2015.
A. Kurnianto and F. Limano, “Visual representation of character of wayang kulit purwa in the wayang-based games: Case studies of Kurusetra and Mahabarat warrior games,” in 2016 1st International Conference on Game, Game Art, and Gamification (ICGGAG), 2016, pp. 1–6.
N. Bonafix, “Designing Wayang Kulit Purwa with Gatotkaca as the Character in Game,” J. Games, Game Art, Gamification, vol. 2, no. 1, 2017.
D. A. Ghani, “Wayang kulit: Digital puppetry character rigging using Maya MEL language,” in 2011 Fourth International Conference on Modeling, Simulation and Applied Optimization, 2011, pp. 1–5.
M. H. Tsai and A. T. E. Hapsari, “Usage Of 12 Animation Principles In The Wayang Kulit Performances,” J. Telemat., vol. 6, no. 1, 2010.
A. Ardiyan and D. Syamsuddin, “Aesthetic Affordances of Buto’s Shape and Texture Characters in Wayang Kulit Through Digital Sculpting,” in 2019 International Conference on Sustainable Engineering and Creative Computing (ICSECC), 2019, pp. 380–385.
R. Jose, “A Convolutional Neural Network (CNN) Approach to detect face using Tensorflow and Keras,” J. Emerg. Technol. Innov. Res., vol. 6, no. 5, pp. 97–103, 2019.
P. Kamencay, M. Benco, T. Mizdos, and R. Radil, “A new method for face recognition using convolutional neural network,” Adv. Electr. Electron. Eng., vol. 15, no. 4 Special Issue, pp. 663–672, 2017.
M. Nimbarte and K. Bhoyar, “Age invariant face recognition using convolutional neural network,” Int. J. Electr. Comput. Eng., vol. 8, no. 4, pp. 2126–2138, Aug. 2018.
Q.-Q. Tao, S. Zhan, X.-H. Li, and T. Kurihara, “Robust face detection using local CNN and SVM based on kernel combination,” Neurocomputing, vol. 211, pp. 98–105, Oct. 2016.
J. Tan et al., “GLCM-CNN: Gray Level Co-occurrence Matrix based CNN Model for Polyp Diagnosis,” in 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), 2019, pp. 1–4.
A. P. Wibawa, H. K. Fithri, I. A. E. Zaeni, and A. Nafalski, “Generating Javanese Stopwords List using K-means Clustering Algorithm,” Knowl. Eng. Data Sci., vol. 3, no. 2, p. 106, Dec. 2020.
Refbacks
- There are currently no refbacks.
Copyright (c) 2021 Aji Prasetya Wibawa
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
___________________________________________________________
International Journal of Visual and Performing Arts
ISSN 2684-9259
Published by Association for Scientific Computing Electronics and Engineering (ASCEE)
W: http://pubs2.ascee.org/index.php/viperarts
E: sularso@ascee.org
Organized by:
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0