KLASIFIKASI POSE YOGA SURYA NAMASKAR MENGGUNAKAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK DENGAN ARSITEKTUR VGG19 DAN RESNET-50

Made Restu Wedatama; Luh Joni Erawati  Dewi; Ni Wayan Marti

doi:10.23960/jitet.v14i1.8824

Authors

Made Restu Wedatama Universitas Pendidikan Ganesha
Luh Joni Erawati Dewi Universitas Pendidikan Ganesha
Ni Wayan Marti Universitas Pendidikan Ganesha

DOI:

https://doi.org/10.23960/jitet.v14i1.8824

Abstract Views: 44 File Views: 20

Keywords:

Deep Learning, CNN, VGG19, ResNet-50, Yoga Surya Namaskar

Abstract

Penelitian ini bertujuan untuk membangun dan membandingkan kinerja model klasifikasi pose yoga Surya Namaskar menggunakan metode Deep Learning dengan arsitektur Convolutional Neural Network (CNN) VGG19 dan ResNet-50. Surya Namaskar merupakan rangkaian pose yoga yang populer, namun kesalahan postur tanpa pengawasan instruktur dapat berisiko cedera. Dataset yang digunakan terdiri dari 154 citra asli yang diaugmentasi menjadi 1.540 citra, terbagi dalam 7 kelas pose: Pranamasana, Hasta Utthanasana, Padahastasana, Ashwa Sanchalanasana, Parvatasana, Ashtanga Namaskara, dan Bhujangasana. Proses pelatihan dilakukan menggunakan optimizer Adam dengan variasi learning rate 0.0001, 0.00001, dan 0.00005. Hasil penelitian menunjukkan bahwa arsitektur ResNet-50 secara konsisten mengungguli VGG19. Kinerja terbaik dicapai oleh ResNet-50 pada learning rate 0.00005 dengan akurasi 85%, sedangkan akurasi terbaik VGG19 hanya mencapai 77% pada learning rate 0.0001. Penelitian menyimpulkan bahwa arsitektur ResNet-50 dengan residual connection lebih efektif dalam mengklasifikasikan pose yoga yang kompleks dibandingkan VGG19, meskipun masih terdapat tantangan dalam membedakan pose dengan kemiripan visual tinggi seperti Ashwa Sanchalanasana dan Bhujangasana.

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