Implementasi Deep Learning sebagai Media Evaluasi Keterampilan Gerak Dasar Atletik dalam Pendidikan Jasmani
DOI:
https://doi.org/10.57008/jjp.v5i04.1893Keywords:
Deep Learning, keterampilan gerak dasar, atletik, pendidikan jasmani, CNNAbstract
Penelitian ini bertujuan untuk mengimplementasikan teknologi Deep Learning sebagai alat evaluasi keterampilan gerak dasar atletik dalam pembelajaran pendidikan jasmani. Penelitian menggunakan metode quasi experiment dengan 60 mahasiswa Pendidikan Jasmani yang dibagi menjadi dua kelompok, yaitu kelompok eksperimen dan kelompok kontrol. Sistem evaluasi dikembangkan menggunakan algoritma Convolutional Neural Network (CNN) yang dilatih untuk mengenali tiga jenis gerak dasar atletik: lari, lompat, dan lempar. Hasil pengujian menunjukkan bahwa sistem memiliki akurasi pengenalan gerak sebesar 95,2%, precision sebesar 94,1%, dan recall sebesar 93,6%. Berdasarkan uji paired sample t-test, terdapat peningkatan signifikan pada nilai keterampilan mahasiswa kelompok eksperimen dari 73,1 menjadi 87,4 (p < 0,05), sedangkan kelompok kontrol meningkat dari 72,8 menjadi 79,2. Tingkat kepuasan dosen terhadap sistem mencapai 90,7%, dengan kategori sangat baik. Hasil ini menunjukkan bahwa Deep Learning efektif digunakan sebagai media evaluasi yang objektif, efisien, dan akurat, serta menjadi inovasi dalam proses pembelajaran dan penilaian pendidikan jasmani.
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