Modified Deep Pattern Classifier on Indonesian Traditional Dance Spatio-Temporal Data
Abstract
Traditional dances, like those of Indonesia, have complex and unique patterns requiring accurate cultural preservation and documentation classification. However, traditional dance classification methods often rely on manual analysis and subjective judgment, which leads to inconsistencies and limitations. This research explores a modified deep pattern classifier of traditional dance movements in videos, including Gambyong, Remo, and Topeng, using a Convolutional Neural Network (CNN). Evaluation model's performance using a testing spatio-temporal dataset in Indonesian traditional dance videos is performed. The videos are processed through frame-level segmentation, enabling the CNN to capture nuances in posture, footwork, and facial expressions exhibited by dancers. Then, the obtained confusion matrix enables the calculation of performance metrics such as accuracy, precision, sensitivity, and F1-score. The results showcase a high accuracy of 97.5%, indicating the reliable classification of the dataset. Furthermore, future research directions are suggested, including investigating advanced CNN architectures, incorporating temporal information through recurrent neural networks, exploring transfer learning techniques, and integrating user feedback for iterative refinement of the model. The proposed method has the potential to advance dance analysis and find applications in dance education, choreography, and cultural preservation.
Downloads
References
Emmanuel C. Maraña, Ramiella Anne A. Arpon, Irvin Lance L. Capuchino, Romiele Anne F. Casiño, Kate Rossleth T. Casuga, Jenifer G. Aguilar. Strengthening of best practices in the preservation of cultural diversities: A phenomenological research. GSC Adv Res Rev 2023; Vol. 15, pp.46–62, 2023 DOI: https://doi.org/10.30574/gscarr.2023.15.3.0166
Shoji M, Takafuji Y, Harada T. Behavioral impact of disaster education: Evidence from a dance-based program in Indonesia. Int J Disaster Risk Reduct, Vol. 45, 2020 DOI: https://doi.org/10.1016/j.ijdrr.2020.101489
Tresnawaty B, Risdayah E. Religion and Media: Anthropological study of religious behavior in the film “Little House on the Prairie”. ETNOSIA J Etnografi Indones, Vol. 8, pp.116–125, 2023 DOI: https://doi.org/10.31947/etnosia.v8i1.22189
Cruz AGB, Seo Y, Scaraboto D. Between Cultural Appreciation and Cultural Appropriation: Self-Authorizing the Consumption of Cultural Difference. J Consum Res, 2023 DOI: https://doi.org/10.1093/jcr/ucad022
Domingues AR, Mazhar MU, Bull R. Environmental performance measurement in arts and cultural organisations: Exploring factors influencing organisational changes. J Environ Manage, Vol 326, 2023 DOI: https://doi.org/10.1016/j.jenvman.2022.116731
Natar M, Age MYC. CACI: The Contradiction Between the Nature and Practice of Modern Manggarai Society with Its Relevance to the Character Formation of the Millennial Generation. Int J Soc Serv Res, Vol. 3, pp. 1166–1172, 2023 DOI: https://doi.org/10.46799/ijssr.v3i5.377
Handayani R, Narimo S, Fuadi D, Minsih M, Widyasari C. Preserving Local Cultural Values in Forming the Character of Patriotism in Elementary School Students in Wonogiri Regency. J Innov Educ Cult Res Vol. 4, pp. 56–64, 2023 DOI: https://doi.org/10.46843/jiecr.v4i1.450
Jain N, Bansal V, Virmani D, Gupta V, Salas-Morera L, Garcia-Hernandez L. An Enhanced Deep Convolutional Neural Network for Classifying Indian Classical Dance Forms. Appl Sci Vol.11, pp. 6253, 2021 DOI: https://doi.org/10.3390/app11146253
Mu J. Pose Estimation-Assisted Dance Tracking System Based on Convolutional Neural Network. Comput Intell Neurosci, pp.1–10. 2022 DOI: https://doi.org/10.1155/2022/2301395
Victoria AH, Maragatham G. Automatic tuning of hyperparameters using Bayesian optimization. Evol Syst 2021, Vol.12, pp.217–223, 2021 DOI: https://doi.org/10.1007/s12530-020-09345-2
Challapalli JR, Devarakonda N. A novel approach for optimization of convolution neural network with hybrid particle swarm and grey wolf algorithm for classification of Indian classical dances. Knowl Inf Syst 2022, Vol.64, pp. 2411–2434, 2022 DOI: https://doi.org/10.1007/s10115-022-01707-3
Odefunso AE, Bravo EG, Chen YV. Traditional African Dances Preservation Using Deep Learning Techniques. Proc ACM Comput Graph Interact Tech 2022, Vpl.5, pp.1–11, 2022 DOI: https://doi.org/10.1145/3533608
Liu M, Jervis M, Li W, Nivlet P. Seismic facies classification using supervised convolutional neural networks and semisupervised generative adversarial networks. GEOPHYSICS, Vol.85, p. 47–58, 2020 DOI: https://doi.org/10.1190/geo2019-0627.1
Ranjbarzadeh R, Dorosti S, Jafarzadeh Ghoushchi S, Safavi S, Razmjooy N, Tataei Sarshar N, et al. Nerve optic segmentation in CT images using a deep learning model and a texture descriptor. Complex Intell Syst, Vol.8, pp. 43–57, 2022 DOI: https://doi.org/10.1007/s40747-022-00694-w
Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and F. F. Li, Large-scale video classification with convolutional neural networks, Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1725-1732, 2014 DOI: https://doi.org/10.1109/CVPR.2014.223
J. Y. H. Ng, M. Hausknecht, S. Vijayanarasimhan, O. Vinyals, R. Monga, and G. Toderici, Beyond short snippets: Deep networks for video classification, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 07, pp. 4694-4702, 2015
A. Yenter and A. Verma, Deep CNN-LSTM with combined kernels from multiple branches for IMDb review sentiment analysis in 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2017, pp. 540-546, 2017 DOI: https://doi.org/10.1109/UEMCON.2017.8249013
E. Ergün, F. Gürkan, O. Kaplan and B. Günsel, Video action classification by deep learning, 2017 25th Signal Processing and Communications Applications Conference (SIU), Antalya, Turkey, pp. 1-4, 2017 DOI: https://doi.org/10.1109/SIU.2017.7960446
M. Abdullah, M. Ahmad and D. Han, Facial Expression Recognition in Videos: An CNN-LSTM based Model for Video Classification, 2020 International Conference on Electronics, Information, and Communication (ICEIC), Barcelona, Spain, pp. 1-3, 2020 DOI: https://doi.org/10.1109/ICEIC49074.2020.9051332
M. A. Russo, A. Filonenko and K. -H. Jo, Sports Classification in Sequential Frames Using CNN and RNN, 2018 International Conference on Information and Communication Technology Robotics (ICT-ROBOT), Busan, Korea (South), pp. 1-3, 2018 DOI: https://doi.org/10.1109/ICT-ROBOT.2018.8549884
Savran Kızıltepe, R., Gan, J.Q. & Escobar, J.J. A novel keyframe extraction method for video classification using deep neural networks. Neural Comput & Applic, 2021 DOI: https://doi.org/10.1007/s00521-021-06322-x
S. Kulhare, S. Sah, S. Pillai and R. Ptucha, Key frame extraction for salient activity recognition, 2016 23rd International Conference on Pattern Recognition (ICPR), Cancun, Mexico, pp. 835-840, 2016 DOI: https://doi.org/10.1109/ICPR.2016.7899739
S. Jadon and M. Jasim, Unsupervised video summarization framework using keyframe extraction and video skimming, 2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA), Greater Noida, India, pp. 140-145, 2020 DOI: https://doi.org/10.1109/ICCCA49541.2020.9250764
H. Wang, F. Nie, H. Huang, and Y. Yang, Learning frame relevance for video classification, Proceedings of the 19th ACM international conference on Multimedia, pp. 1345–1348, 2011. DOI: https://doi.org/10.1145/2072298.2072011
Hegarini, E., Dharmayanti, Syakur, A.. Indonesian traditional dance motion capture documentation. 2016 2nd International Conference on Science and Technology-Computer (ICST), pp.108-111. 2016. DOI: https://doi.org/10.1109/ICSTC.2016.7877357
Jaya, I.K.H.T, Kesiman, M.W.A, Sunarya, I.M.G., Detecting the Same Pattern in Choreography Balinese Dance Using Convolutional Neural Network and Analysis Suffix Tree, Scientific Journal of Electrical Engineering Computers and Informatics, Vol 8, No 3, 2022. DOI: https://doi.org/10.26555/jiteki.v8i3.24461
Ibrahim; Abdul, Rachmat, Pakarena dance image classification using convolutional neural network algorithm, ILKOM Scientific Journal, Vol. 13, No. 2, pp. 134-139, 2021 DOI: https://doi.org/10.33096/ilkom.v13i2.816.134-139
Kishore, P.V.V., Kumar, K V K , Eepuri, Kiran & Sastry, A , Maddala, Teja , Anil Kumar, D. & Prasad, M.V.D.. (2018). Indian Classical Dance Action Identification and Classification with Convolutional Neural Networks. Advances in Multimedia, pp. 1-10, 2018. DOI: https://doi.org/10.1155/2018/5141402
Nazari, H., Kaynak, S, Classification of Turkish Folk Dances using Deep Learning. International Journal of Intelligent Systems and Applications in Engineering, pp. 226–232, 2022.
Shailesh, S, Judy, M.V., Understanding dance semantics using spatio-temporal features coupled GRU networks. Entertain Comput., pp. 484, 2022.
Tari Topeng Sekartaji Isi Surakarta, Indonesia Traditional Mask Dance - YouTube n.d. https://www.youtube.com/watch?v=TioUqzaqrj8 (accessed August 3, 2023).
Anglep Praba Candrasmurti_Tari Gambyong Pangkur - YouTube n.d. https://www.youtube.com/watch?v=NjobG1Ptd1o (accessed August 3, 2023)
Tari Remo Gagrak Anyar - YouTube n.d. https://www.youtube.com/watch?v=RbeyfjuA8ew (accessed August 3, 2023)
Ghosh A, Sufian A, Sultana F, Chakrabarti A, De D. Fundamental Concepts of Convolutional Neural Network. In: Balas VE, Kumar R, Srivastava R, editors. Recent Trends Adv. Artif. Intell. Internet Things, vol. 172, Cham: Springer International Publishing, pp. 519–67, 2020 DOI: https://doi.org/10.1007/978-3-030-32644-9_36
Khan A, Sohail A, Zahoora U, Qureshi AS. A survey of the recent architectures of deep convolutional neural networks. Artif Intell Rev, Vol. 53, pp. 455–516, 2020 DOI: https://doi.org/10.1007/s10462-020-09825-6
Michael Onyema E, Balasubaramanian S, Suguna S K, Iwendi C, Prasad BVVS, Edeh CD. Remote monitoring system using slow-fast deep convolution neural network model for identifying anti-social activities in surveillance applications. Meas Sens, Vol. 27, 2023 DOI: https://doi.org/10.1016/j.measen.2023.100718
Diwan T, Anirudh G, Tembhurne JV. Object detection using YOLO: challenges, architectural successors, datasets and applications. Multimed Tools Appl 2023, Vol. 82, pp. 243–275, 2023 DOI: https://doi.org/10.1007/s11042-022-13644-y
Daviran M, Shamekhi M, Ghezelbash R, Maghsoudi A. Landslide susceptibility prediction using artificial neural networks, SVMs and random forest: hyperparameters tuning by genetic optimization algorithm. Int J Environ Sci Technol, Vol.20, pp.259–276, 2023 DOI: https://doi.org/10.1007/s13762-022-04491-3
Ajayi OG, Ashi J. Effect of varying training epochs of a Faster Region-Based Convolutional Neural Network on the Accuracy of an Automatic Weed Classification Scheme. Smart Agric Technol, Vol.3, 2023 DOI: https://doi.org/10.1016/j.atech.2022.100128
Kim S-C, Cho Y-S. Predictive System Implementation to Improve the Accuracy of Urine Self-Diagnosis with Smartphones: Application of a Confusion Matrix-Based Learning Model through RGB Semiquantitative Analysis. Sensors, Vol. 22, 2022 DOI: https://doi.org/10.3390/s22145445
Kara OC, Venkatayogi N, Ikoma N, Alambeigi F. A Reliable and Sensitive Framework for Simultaneous Type and Stage Detection of Colorectal Cancer Polyps. Ann Biomed Eng, Vol.51, pp.1499–1512., 2022 DOI: https://doi.org/10.1007/s10439-023-03153-w
Copyright (c) 2023 EMITTER International Journal of Engineering Technology
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The copyright to this article is transferred to Politeknik Elektronika Negeri Surabaya(PENS) if and when the article is accepted for publication. The undersigned hereby transfers any and all rights in and to the paper including without limitation all copyrights to PENS. The undersigned hereby represents and warrants that the paper is original and that he/she is the author of the paper, except for material that is clearly identified as to its original source, with permission notices from the copyright owners where required. The undersigned represents that he/she has the power and authority to make and execute this assignment. The copyright transfer form can be downloaded here .
The corresponding author signs for and accepts responsibility for releasing this material on behalf of any and all co-authors. This agreement is to be signed by at least one of the authors who have obtained the assent of the co-author(s) where applicable. After submission of this agreement signed by the corresponding author, changes of authorship or in the order of the authors listed will not be accepted.
Retained Rights/Terms and Conditions
- Authors retain all proprietary rights in any process, procedure, or article of manufacture described in the Work.
- Authors may reproduce or authorize others to reproduce the work or derivative works for the author’s personal use or company use, provided that the source and the copyright notice of Politeknik Elektronika Negeri Surabaya (PENS) publisher are indicated.
- Authors are allowed to use and reuse their articles under the same CC-BY-NC-SA license as third parties.
- Third-parties are allowed to share and adapt the publication work for all non-commercial purposes and if they remix, transform, or build upon the material, they must distribute under the same license as the original.
Plagiarism Check
To avoid plagiarism activities, the manuscript will be checked twice by the Editorial Board of the EMITTER International Journal of Engineering Technology (EMITTER Journal) using iThenticate Plagiarism Checker and the CrossCheck plagiarism screening service. The similarity score of a manuscript has should be less than 25%. The manuscript that plagiarizes another author’s work or author's own will be rejected by EMITTER Journal.
Authors are expected to comply with EMITTER Journal's plagiarism rules by downloading and signing the plagiarism declaration form here and resubmitting the form, along with the copyright transfer form via online submission.
