Implementasi Arsitektur InceptionResNet-v2 dan Squared Hinge Loss (Studi Kasus Klasifikasi Pose Yoga)

MUHAMMAD ICHWAN, ANNISA OLGA ZERLINDA

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ABSTRAK

Aplikasi computer vision dapat digunakan untuk mengurangi kecelakaan yang terjadi ketika melakukan yoga akibat postur tubuh saat melakukan yoga yang tidak tepat. Pada penelitian, dilakukan sebagai langkah awal dalam penentuan model klasifikasi yang digunakan pada aplikasi yang dapat melakukan koreksi postur tubuh saat yoga. Penelitian dilakukan dengan mengklasifikasikan 11 studi kasus pose yoga dengan mengimplementasikan arsitektur InceptionResnet-v2 dan Squared Hinge Loss. Pada hasil yang didapatkan, model dengan kinerja terbaik diperoleh pada learning rate 0.0001, epoch  200, scaling residual InceptionResnet blok A 0.15, blok B 0.1, dan blok C 0.2, serta blok InceptionResnet 5 iterasi, B 10 iterasi, dan C 5 iterasi dengan kinerja arsitektur berdasarkan hasil evaluasi performa model didapatkan 89.98% accuracy, 90.38% precision, 89.79% recall, 89.83% F1 score, and 99% specificity pada pengujian 888 data uji dengan 11 pose yoga berbeda. Rata-rata pengujian waktu klasifikasi 1.301s dan loss 0.9494.

Kata kunci: CNN, InceptionResnet-v2, Klasifikasi Citra, Squared Hinge Loss, Yoga

ABSTRACT

Computer vision applications can be used to reduce accidents caused by improper posture while doing yoga. In this study, it was carried out as a first step in determining the classification model used in applications that can make corrections to a body posture while doing yoga. The research was conducted to classify 11 case studies of yoga poses by implementing the InceptionResnet-v2 architecture and Squared Hinge Loss. In the results, the model with the best performance was obtained at a learning rate of 0.0001, epoch 200, scaling residual InceptionResnet block A 0.15, block B 0.1, and block C 0.2, and 5 iteration of InceptionResnet block A, 10 iterations of block B, and 5 iterations of block C. The results of the model performance evaluation obtained 89.98% accuracy, 90.38% precision, 89.79% recall, 89.83% F1 score, and 99% specificity in the test of 888 test data with 11 different yoga poses and 1.301s average testing time of the classification model and loss 0.9494.

Keywords: CNN, Image Classification, InceptionResnet-v2, Squared Hinge Loss, Yoga


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Referensi


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DOI: https://doi.org/10.26760/mindjournal.v7i2.124-138

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