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All Journal Techno.Com: Jurnal Teknologi Informasi JAIS (Journal of Applied Intelligent System) EMITTER International Journal of Engineering Technology Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika Creative Information Technology Journal Voice Of Informatics Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Pengabdian Literasi Digital Indonesia
Mulyanto, Edy
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Social Sciences

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Journal : EMITTER International Journal of Engineering Technology

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Modified Deep Pattern Classifier on Indonesian Traditional Dance Spatio-Temporal Data Mulyanto, Edy; Yuniarno, Eko Mulyanto; Hafidz, Isa; Budiyanta, Nova Eka; Priyadi, Ardyono; Hery Purnomo, Mauridhi
EMITTER International Journal of Engineering Technology Vol 11 No 2 (2023)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v11i2.832

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.
Co-Authors Abu Salam Ardianto, Christian Ardyono Priyadi Catur Supriyanto Deny Salvana Ervan Didik Srianto Edi Sugiarto Eko Mulyanto Yuniarno Erlin Dolphina Fahmi Amiq Fikri Budiman Hanny Haryanto Isa Hafidz Karis Widyatmoko Mardiantara, Naya Alifiah az Azar Putri Mauridhi Hery Purnomo Muhammad Sofi'i Muhammad Sofi’i, Muhammad Nova Eka Budiyanta Octaviani, Dhita Aulia Paramita, Cinantya Rahmadianto, Rizky Rohman, Muhammad Syaifur Saraswati, Galuh Wilujeng Sindhu Rakasiwi T. Sutojo Vera Pebrianasari Wibowo, Gamma Wira Winarsih, Nurul Anisa Sri Wisinggya, Kadhana Reya Yuventius Tyas Catur Pramudi