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All Journal JURNAL PENELITIAN ILMU PENDIDIKAN Medikora: Jurnal Ilmiah Kesehatan Olahraga Journal of Education and Learning (EduLearn) Jurnal Kesehatan Masyarakat Jurnal Keolahragaan Indonesian Journal of Science and Technology Jurnal Terapan Ilmu Keolahragaan JSKK (Jurnal Sains Keolahragaan dan Kesehatan) Jurnal Pendidikan Kesehatan Rekreasi Jurnal Speed (Sport, Physical Education, Empowerment) Indonesian Journal of Sport Management Jurnal Abdimas Kartika Wijayakusuma Indonesian Journal of Teaching in Science International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) International Journal of Research and Applied Technology (INJURATECH) Jurnal Kesehatan dan Olahraga JURNAL PENDIDIKAN OLAHRAGA
Nur Indri Rahayu
Universitas Pendidikan Indonesia
Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Hubungan screen time dan tingkat aktivitas fisik mahasiswa di masa covid-19 dengan health related quality of life Raden Cyntani Araya; Yati Rukhayati; Imas Damayanti; Adang Suherman; Nur Indri Rahayu; Jajat Jajat; Kuston Sultoni
MEDIKORA Vol 21, No 1 (2022): April
Publisher : Faculty of Sports Sciences, Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/medikora.v21i1.47258

Abstract

Penelitian ini bertujuan untuk menguji hubungan screen time dan tingkat aktivitas fisik mahasiswa di masa covid-19 dengan health related quality of life. Metode yang digunakan dalam penelitian ini deskriptif korelasi dengan pendekatan kuantitatif. Sampel dalam penelitian sebanyak 360 orang mahasiswa aktif Universitas Pendidikan Indonesia. Instrumen pengambilan data mengunakan Global Physical Activity Questionnaire (GPAQ), Questionnaire For Screen Time Of Adolescents (QUEST), dan Health Related Quality Of Life SF-36 (HRQoL SF-36). Hasil dari analisis data yng diketahui bahwa dapat disimpulkan bahwa rata- rata MET mahasiswa UPI pada pandemi COVID- 19 berkisar 1027 MET. Level aktivitas fisik mahasiswa UPI pada pandemi COVID-19 tergolong sedang (nilai MET 600- 3000). Terdapat 8 aspek dalam kualitas hidup (HRQOL SF-36). Berikut 8 kualitas hidup yang terdiri dari: fungsi fisik, peran fisik, rasa nyeri, kesehatan umum, fungsi sosial, vitalitas, peran emosi, kesehatan mental. Hasil pengolahan data dalam penelitian ini menunjukan bahwa terdapat hubungan yang signifikan antara aktivitas fisik dan HRQOL dikarenakan nilai P = 0,000 0,05. Hal ini menunjukkan bahwa aktifitas fisik dapat menjadi salah satu faktor penyumbang kualitas hidup, tetapi screen time tidak menunjukan hubungan yang signifikan karena nilai P = 0,762 0,05.The relationship of screen time and physical activity level during covid-19 with health-related quality of life  among university studentsAbstractThis study aimed to test the relationship between screen time and physical activity levels of students during the Covid-19 period with health-related quality of life. The method used in this research was a descriptive correlation with a quantitative approach. The sample in the study was 360 active students at an Indonesian Education University. Physical activity was assessed using the Global Physical Activity Questionnaire (GPAQ). Screen time was measured using the Questionnaire For Screen Time of Adolescents (QUEST), and quality of life was assessed with the Health Related Quality of Life SF-36 (HRQoL SF-36). The results of the data analysis showed that the average the total metabolic equivalent of task (MET) per week  was 1027 MET, thus the level, was classified as moderate. There  were significant relationship between each 8 HRQoL subscale (i.e. Physical Function, Physical Role, Pain, General Health, Social Function, Vitality, Emotional Role, Mental Health) and. physical activity and HRQOL ( P value 0.000).  No significant correlation however was found between physical activity and screen time. This indicates that physical activity can be a contributing factor to quality of life, but screen time does not show a significant relationship because P value = 0.762 0.05.  
Hubungan physical activity dengan fine motor skills pada anak usia 4 tahun Nur Indri Rahayu; Aini Dewi Monica; Jajat Jajat; Kuston Sultoni
Jurnal Keolahragaan Vol 9, No 1: April 2021
Publisher : Program Studi Ilmu Keolahragaan Program Pascasarjana Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1851.807 KB) | DOI: 10.21831/jk.v9i1.34156

Abstract

Tujuan penelitian ini adalah menguji hubungan antara physical activity dengan fine motor skills pada anak usia 4 tahun. Metode yang digunakan adalah metode penelitian kuantitatif dengan pendekatan korelasional. Populasi dalam penelitian ini yaitu anak usia 4 tahun yang sedang menempuh pendidikan anak usia dini di PAUD, TK, dan KB di Kota Bandung. Jumlah sampel sebanyak 53 anak dengan teknik pengambilan sampel menggunakan purposive sampling. Instrumen yang digunakan berupa Accelerometer Actigraph dan 9-Hole Peg Test. Accelerometer Actigraph digunakan untuk mengukur tingkat physical activity atau aktivitas fisik dengan hasil yang menunjukan bahwa anak – anak paling banyak menghabiskan waktu di skor light daripada sedentary, moderate-to-vigorous dan vigorous. 9-Hole Peg Test digunakan untuk mengukur tingkat kemampuan motorik halus atau fine motor skills anak dengan hasil menunjukan bahwa anak lebih terampil dalam menggunakan tangan yang dominan. Data kemudian dianalisis dengan menggunakan Spearman Correlation Test. Hasil analisis data menunjukan tidak terdapat korelasi antara physical activity dengan fine motor skills baik pada tangan dominan (p=0,6780,05) maupun dengan tangan non dominan (p=0,1670,05) yang berarti tidak terdapat hubungan yang signifikan antara physical activity dengan fine motor skills pada anak usia 4 tahun. The relationship between physical activity and fine motor skills in 4-year-old children Abstract:The purpose of this study was to examine the relationship between physical activity and fine motor skills in 4-year-old children. The method used is a quantitative research method with the correlation research approach. The population in this study were 4-year-old children who were taking early education in PAUD, TK, and KB in Bandung City. A total of 53 4-year-old children participated in this study by using a purposive sampling technique. The instrumen used were Accelerometer Actograph and 9-Hole Peg Test. The accelerometer actigraph is used to measure the level of physical activity and the results show that children spend the most time on the light score rather than sedentary, moderate-to-vigorous and vigorous score. 9-Hole Peg Test is used to measure the level of fine motor skills of children and the results showing that children are more skilled in using the dominant hand. Data were analyzed using the Spearman Correlation Test. The results of data analysis showed there is no correlation between physical activity and fine motor skills both in dominant hand (p=0.6780,05) and with the non-dominant hand (p=0,1670,05) which meant there are no significant relationship between physical activity and fine motor skills in 4-year-old children.
Phytochemical Profile and Biological Activities of Ethylacetate Extract of Peanut (Arachis hypogaea L.) Stems: In-Vitro and In-Silico Studies with Bibliometric Analysis Idin Sahidin; N. Nohong; Marianti A. Manggau; A. Arfan; W. Wahyuni; Iren Meylani; M. Hajrul Malaka; Nur Syifa Rahmatika; Agung W. M. Yodha; Nur Upik En Masrika; Abdulkadir Kamaluddin; Andini Sundowo; Sofa Fajriah; Rathapon Asasutjarit; Adryan Fristiohady; Rina Maryanti; Nur Indri Rahayu; M. Muktiarni
Indonesian Journal of Science and Technology Vol 8, No 2 (2023): (ONLINE FIRST) IJOST: September 2023
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/ijost.v8i2.54822

Abstract

The utilization of the stems, leaves, and hulls of peanuts (Arachis hypogea) is not as popular as the seeds. This study aimed to investigate the chemical contents and pharmacological activities of A. hypogaea stems in-vitro and in-silico. This study was also completed with bibliometric analysis. The methanol extract (ME) was reextracted by ethylacetate to get ethyl acetate extract (EAE). The chemical contents of EAE were analyzed by phytochemical screening, Liquid Chromatography-Mass Spectroscopy (LC-MS/MS), TPC (Total Phenolic Content), and TFC (Total Flavonoid Content). In-vitro and in-silico studies evaluated antioxidant potency, toxicity, and cytotoxicity toward MCF-7 cell lines. The results showed that EAE contained terpenoids, flavonoids, alkaloids, and phenolics which were supported by LC-MS/MS data. The EAE was categorized as a very strong antioxidant and moderately active in both cytotoxicity and toxicity.
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.
Analisis Promosi Gaya Hidup Sehat dan Aktif pada Perguruan Tinggi Negeri di Jawa Barat Muhammad Dzulfikar Firdaus; Adang Suherman; Jajat; Surdiniaty Ugelta; Yati Ruhayati; Kuston Sultoni; Imas Damayanti; Mohammad zaky; Nur Indri Rahayu
JURNAL PENDIDIKAN OLAHRAGA Vol 14 No 2 (2024): JURNAL PENDIDIKAN OLAHRAGA
Publisher : STKIP Taman Siswa Bima

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37630/jpo.v14i2.1612

Abstract

Gaya hidup sehat memiliki pengaruh yang besar dalam kesehatan dan kebugaran yang menjadi faktor penting dalam menentukan kesehatan dan penyakit seseorang, bahkan gaya hidup sehat berdampak pada peningkatan kesejahteraan seseorang. Penelitian ini bertujuan untuk menganalisis dan mengevaluasi promosi gaya hidup sehat dan aktif pada mahasiswa perguruan tinggi negeri di Jawa Barat. Metode penelitian yang digunakan adalah cross sectional dengan menggunakan kuesioner Health Promoting Lifestyle Profile II. Partisipan terdiri dari 641 mahasiswa yang berusia antara 18 tahun sampai dengan 24 tahun (M =21,05 ± SD= 1,369) yang terdiri dari 326 laki-laki dan 315 perempuan dan partisipan dipilih melalui teknik purposive sampling. Analisis data pada penelitian ini menggunakan descriptive statistics untuk mengetahui jumlah partisipan berdasarkan karakteristik partisipan, sementara itu, independent samples t-test dilakukan untuk mengetahui perbedaan rata-rata skor gaya hidup sehat dan aktif berdasarkan jenis kelamin dan status tempat tinggal dan one way ANOVA untuk mengetahui perbedaan berdasarkan status tempat tinggal. Hasil penelitian menunjukkan adanya perbedaan signifikan antara mahasiswa berdasarkan jenis kelamin yang memiliki nilai (p < 0,05) dan jenis UKM yang diikuti dengan nilai (p < 0,05), sementara itu tidak terdapat perbedaan yang signifikan berdasarkan status tempat tinggal yang memiliki nilai (p > 0,05). Dengan demikian, promosi gaya hidup sehat dan aktif harus terus dilakukan untuk meningkatkan gaya hidup sehat dan aktif pada mahasiswa.
A Machine Learning Approach to Predicting Physical Activity Levels in Adolescents Mahendra, Desvy Rahma Putri; Jajat, Jajat; Damayanti, Imas; Sultoni, Kuston; Ruhayati, Yati; Suherman, Adang; Rahayu, Nur Indri
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.v3i2.7145

Abstract

The ongoing evolution of technology has had both positive and negative effects on modern society. On the positive side, it has significantly improved the ease with which various activities can be performed. However, it has also had a negative impact by reducing physical activity. This reduction in physical activity, in turn, increases the risk of chronic diseases that contribute to global mortality rates. This research aims to assess the effectiveness of machine learning in predicting the physical activity levels of adolescents. The study utilizes data from accelerometers, specifically the ActiGraph GT3X. The research methodology employs a semi-supervised machine learning approach, using the support vector machine and decision tree algorithms to make these predictions. The sample comprises 61 adolescents (males = 17, female = 44), including high school students and university students aged 18-21, from the West Java region. The results from the machine learning model using the decision tree algorithm indicated a model accuracy of 97.50% in predicting physical activity levels. In contrast, the accuracy obtained from the performance analysis using the confusion matrix for the support vector machine model was 92.5%. Based on these accuracy levels, the decision tree algorithm outperforms the support vector machine algorithm's accuracy. Further analyses involving different models are necessary to determine which algorithm offers the highest level of accuracy.
Classifying Physical Activity Levels in Early Childhood Using Actigraph and Machine Learning Method Wandani, Syifa; Suherman, Adang; Jajat; Sultoni, Kuston; Ruhayati, Yati; Damayanti, Imas; Rahayu, Nur Indri
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.
RELIABILITAS PITTSBURGH SLEEP QUALITY INDEX VERSI BAHASA INDONESIA PADA LANSIA AKTIF BEROLAHRAGA Sadewa, Fanuelciho; Ruhayati, Yati; Jajat, Jajat; Sultoni, Kuston; Suherman, Adang; Damayanti, Imas; Rahayu, Nur Indri
Jurnal Kesehatan dan Olahraga Vol 8, No 1 (2024)
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/ko.v8i1.56927

Abstract

Seiring bertambahnya usia, volume dan kualitas tidur biasanya akan semakin berkurang. Kualitas tidur salah satunya dikaitkan dengan aktivitas fisik dan olahraga. Namun demikian untuk mengukur kualitas tidur pada kelompok spesifik populasi yang aktif berolahraga masih terbatas, khususnya di Indonesia. Tujuan penelitian ini yaitu menguji reliabilitas dan validitas Pittsburgh Sleep Quality Index (PSQI) versi Indonesia. Pengujian validitas dan reliabilitas dilakukan tiga tahap, yaitu validitas bahasa, validitas & reliabilitas keterbacaan serta validitas & reliabilitas konstruk. Penelitian ini melibatkan 200 orang partisipan lansia berusia 60 – 77 tahun yang aktif di klub olahraga. Pengolahan dan analisis data dengan menggunakan correct item total correlation dan Cronbach’s alpha. Hasil penelitian menunjukkan bahwa PSQI versi Bahasa Indonesia pada populasi lansia yang aktif di klub olahraga memiliki reliabilitas yang rendah nilai Cronbach’s Alpha 0,4. Metode analisis seperti confirmatory factor analysis diperlukan untuk penelitian lebih lanjut.
Prediksi BMI Berdasarkan Level Aktivitas Fisik dengan Metode Analisis Machine Learning Saputra, Diki Saputra; Jajat; Damayanti, Imas; Sultoni, Kuston; Ruhayati, Yati; Rahayu, Nur Indri
Jurnal Pendidikan Kesehatan Rekreasi Vol. 10 No. 1 (2024): Januari 2024
Publisher : Program Studi Pendidikan Jasmani Kesehatan dan Rekreasi FKIP Universitas PGRI Mahadewa Indonesia bekerjasama dengan Asosiasi Prodi Olahraga Perguruan Tinggi PGRI (APOPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59672/jpkr.v10i1.3499

Abstract

Prevalensi obesitas telah menjadi salah satu isu global dalam bidang kesehatan di masyarakat. Sementara itu aktivitas fisik diakui menjadi salah satu yang memiliki peran penting dalam mengatasi prevalensi obesitas. Tujuan penelitian ini yaitu untuk menjelaskan hubungan aktivitas fisik dengan Body Mass Index (BMI) dengan metode ML yang saat ini tengah populer. Sumber data yang digunakan yaitu dari kelompok bidang keilmuan sport and physical activity program studi Ilmu Keolahragaan, Universitas Pendidikan Indonesia. Total 212 (usia 19-23 tahun) partisipan yang memenuhi kriteria, terlibat dalam penelitian ini. IPAQ-SF digunakan untuk memperoleh data terkait dengan aktivitas fisik partisipan. Empat metode algoritma ML yaitu decision tree, naïve bayes, k-nearest neighbors (KNN), dan random forest digunakan untuk menganalisis data. Hasil penelitian menunjukkan bahwa algoritma naive bayes memiliki performa paling unggul (akurasi = 52,38%; sensitifitas = 51,65%; spesifisitas = 53,33%) dari ketiga model ML lainnya, sementara KNN paling rendah baik akurasi, sensitifitas, maupun spesifisitas (42,86%) dalam memprediksi BMI berdasarkan level aktivitas fisik. Aktivitas fisik memiliki peran penting dalam memprediksi BMI selain faktor lainnya seperti jenis kelamin dan perilaku sedentary.
KLASIFIKASI AKTIVITAS FISIK BERBASIS DATA ACCELEROMETER DAN KUESIONER DENGAN METODE MACHINE LEARNING Putri Mulyana, Humaira Azzahra; Suherman, Adang; Jajat, Jajat; Damayanti, Imas; Sultoni, Kuston; Ruhayati, Yati; Rahayu, Nur Indri
Jurnal Speed (Sport, Physical Education, Empowerment) Vol 6 No 2 (2023): Jurnal Speed (Sport, Physical Education, and Empowerment)
Publisher : Universitas Singaperbangsa Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35706/jurnalspeed.v6i2.10197

Abstract

Accelerometer dan kuesioner merupakan instrumen yang telah banyak digunakan para peneliti dalam studi aktivitas fisik. Penelitian ini bertujuan untuk menganalisis perbedaan akurasi klasifikasi level aktivitas fisik dengan metode machine learning. Partisipan dalam penelitian ini yaitu remaja berusia 18-21 tahun (M=19,79 ; SD = 1,13)  dengan jumlah perempuan 44 orang dan laki-laki 17 orang. Instrumen yang digunakan dalam penelitian yaitu accelerometer Actigraph GT3X dan International Physical Activity Questionnaire (IPAQ). Adapun analisis klasifikasi level aktivitas fisik dilakukan dengan algoritma machine learning decision tree. Hasil analisis menunjukkan bahwa untuk dataset berbasis accelerometer Actigraph GT3X memiliki performa akurasi 98,36%, sedangkan akurasi dataset IPAQ menunjukkan performa akurasi sebesar 73,77%. Metode algoritma machine learning decision tree dapat digunakan untuk mengklasifikasi level aktivitas fisik pada kedua jenis sumber dataset dengan performa akurasi sedang sampai tinggi. Analisis lebih lanjut diperlukan dengan menggunakan algoritma machine learning lainnya untuk mendapatkan hasil penelitian yang lebih variatif. Keywords: Actigraph, Artificial Intelligence, DecisionTree, Intensitas Aktivitas Fisik
Co-Authors Abdulkadir Kamaluddin Adang Suherman Adryan Fristiohady Affero Ismail, Affero Agung W. M. Yodha Agus Rusdiana,, Agus Aini Dewi Monica Al Husaeni, Dwi Fitria Amalia, Triska Pangestu Andini Sundowo Anira Arfan Arfan Asep Bayu Dani Nandiyanto Dr. Tite Juliantine,M.Pd Fazari, Muhamad Firmansyah, Iqbal Hariyanti, Mila Ayu Hendya Alif Junanda Heri setiawan Ida Hamidah Idin Sahidin Iman, Ikhwan Maolana Ibrahim Imas Damayanti Iren Meylani Isma Widiaty Jajat Jajat Jajat, Jajat Kartini, Dini Siti Cahya M. Hajrul Malaka Mahendra, Desvy Rahma Putri Marianti A. Manggau Masrika, Nur Upik En Mohammad Zaky Mohammad Zaky Muhammad Dzulfikar Firdaus Muktiarni, Muktiarni N. Nohong Ningrum, Destiana Ayu Nur Syifa Rahmatika Nuria Haristiani Nurlan Kusmaedi Pitriani, Pipit Putri Mulyana, Humaira Azzahra Raden Cyntani Araya Rathapon Asasutjarit Rina Maryanti Ruhayati, Yati Sadewa, Fanuelciho Saputra, Diki Saputra Sofa Fajriah Sri Devi, Sri Sultoni, Kuston Surdiniaty Ugelta Syarifatunnisa Syarifatunnisa Syifa Wandani Wahyuni Wahyuni Wandani, Syifa Wibowo, Satrio Anggoro Putra Yati Rukhayati