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All Journal Jurnal Terapan Ilmu Keolahragaan Indonesian Journal of Sport Management
Yati Ruhayati
Universitas Pendidikan Indonesia
Arts Humanities Education Public Health Social Sciences Other

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Gambaran Konsumsi Gizi Mahasiswa Program Studi Ilmu Keolahragaan Universitas Pendidikan Indonesia Imas Damayanti; Yati Ruhayati
JTIKOR (Jurnal Terapan Ilmu Keolahragaan) Vol 5, No 2 (2020): Jurnal Terapan Ilmu Keolahragaan
Publisher : Program Studi Ilmu Keolahragaan - Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/jtikor.v5i2.21796

Abstract

Penelitian ini bertujuan untuk mengetahui gambaran konsumsi gizi mahasiswa program studi ilmu keolahragaan di Universitas Pendidikan Indonesia. Metode yang digunakan adalah food recall 24 jam. Hasil Penelitian ini menunjukkan bahwa pola konsumsi gizi mahasiswa program studi ilmu keolahragaan kurang baik. Hampir semua variabel gizi tidak dipenuhi angka kecukupan gizinya. Jumlah kecukupan energi, lemak, karbohidrat dan serat baik pada subjek laki-laki maupun perempuan tidak tercapai. Bahan kebutuhan serat hanya dipenuhi sekitar 20-30 persennya. Namun secara unik kebutuhan protein terpenuhi, bahkan sedikit di atas angka kecukupan gizi.
Classifying Physical Activity Levels in Early Childhood Using Actigraph and Machine Learning Method Syifa Wandani; Adang Suherman; Jajat; Kuston Sultoni; Yati Ruhayati; Imas Damayanti; Nur Indri Rahayu
Indonesian Journal of Sport Management Vol. 3 No. 2 (2023): Indonesian Journal of Sport Management
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/ijsm.v3i2.7173

Abstract

Actigraph is a widely used accelerometer for classifying physical activity levels in children, adolescents, adults, and older people. The classification of physical activity levels on Actigraph is determined through time calculations using cut-point formulas. The study aims to classify physical activity in young children according to the guidelines of the World Health Organization (WHO) using accelerometer data and machine learning methods. The study involved 52 young children (26 girls and 26 boys) aged 4 to 5 years in West Java, with an average age of 4.58 years. Physical activity and sedentary behavior of these early childhood were simultaneously recorded using the Actigraph GT3X accelerometer for seven days. The data from the Actigraph were analyzed using two algorithm models: the decision tree and support vector machine, with the Rapidminer application. The results from the decision tree model show a classification accuracy of 96.00% in categorizing physical activities in young children. On the other hand, the support vector machine model achieved an accuracy of 84.67% in classifying physical activities in young children. The decision tree outperforms the support vector machine in accurately classifying physical activities in early childhood. This research highlights the potential benefits of machine learning in sports and physical activity sciences, indicating the need for further development.
Co-Authors Adang Suherman Imas Damayanti Jajat Kuston Sultoni Nur Indri Rahayu Syifa Wandani