Integrasi Teknologi Deep Learning dalam Pengukuran Kebugaran Fisik Siswa Sekolah Menengah Atas
DOI:
https://doi.org/10.65310/8zgs6b61Kata Kunci:
Pembelajaran mendalam, kebugaran fisik, siswa sekolah menengah, kecerdasan buatan, pendidikan jasmani.Abstrak
Penelitian ini bertujuan untuk mengintegrasikan teknologi deep learning guna mengukur tingkat kebugaran fisik siswa sekolah menengah atas secara lebih akurat dan efisien dibandingkan metode manual tradisional. Sebanyak 240 siswa dari tiga sekolah ikut serta dalam penilaian lima komponen kebugaran: daya tahan kardiovaskular, kekuatan otot, kelenturan, kecepatan, dan indeks massa tubuh (BMI). Sistem berbasis Jaringan Saraf Konvolusional (CNN) digunakan untuk menganalisis data video gerakan siswa dan mengevaluasi tingkat kebugaran mereka. Hasil menunjukkan bahwa model deep learning mencapai akurasi 94,6% dibandingkan dengan penilaian manual oleh pelatih profesional, sambil mengurangi waktu penilaian sebesar 62% (dari 25 menit menjadi 9,5 menit per siswa) dan meningkatkan konsistensi antarpenilai dari 0,71 menjadi 0,93. Selain itu, 87% guru pendidikan jasmani melaporkan bahwa sistem ini sangat bermanfaat untuk penilaian dan dokumentasi. Temuan ini menunjukkan bahwa integrasi deep learning meningkatkan akurasi, efisiensi, dan objektivitas penilaian kebugaran fisik, serta memiliki potensi besar untuk penerapan yang lebih luas dalam pendidikan jasmani berbasis teknologi.
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