Implementasi Algoritma Support Vector Machine Terhadap Klasifikasi Pose Balet
- Romindo Romindo Universitas Pelita Harapan, Jakarta, Indonesia
-
Okky Putra Barus
*
Universitas Pelita Harapan, Jakarta,
Indonesia
- Jefri Junifer Pangaribuan Universitas Pelita Harapan, Jakarta, Indonesia
- Yudhistira Adhitya Pratama Universitas Pelita Harapan, Jakarta, Indonesia
- Evelyn Wiliem Universitas Pelita Harapan, Jakarta, Indonesia (*) Corresponding Author
Abstract
Ballet is considered as one of the most difficult dance due to its technical posture demanded. If performed without guidance it may cause bad posture to ballerina and some serious injuries. A model in identifying different ballet poses is developed with artificial intelligence in order to tear down this barrier. The main purpose of this paper is to demonstrate a methodology that simplified Ballet Pose Recognition using an opensource framework called MediaPipe and a machine learning algorithm called Support Vector Machine. How the model work is it will pass through two stages: first, it extracts data points from an image dataset using the MediaPipe Pose Estimation library, and then it preprocesses the data, trains, validates, and tests it using the Support Vector Machine algorithm to do some pose classification. The model is trained in seven distinct ballet poses, including First Position, Second Position, Third Position, Fourth Position, Fifth Position, Tendu Devant, and Tendu Derrière. This is purposely done in order to assess the competence of the classification model. An accuracy score of 87% is achieved from the ballet pose classification model and is developed to work on images and live videos.
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Copyright (c) 2022 Romindo Romindo, Okky Putra Barus, Jefri Junifer Pangaribuan, Yudhistira Adhitya Pratama, Evelyn Wiliem
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