Article Per Year (5 Year)
p-Index From 2021 - 2026
1.235
P-Index
This Author published in this journals
All Journal Jurnal Pendidikan Kesehatan Rekreasi Indonesian Journal of Sport Management Journal of Physical Education Health and Sport JSSF : Journal of Sport Science and Fitness
Jajat
Unknown Affiliation
Arts Humanities Education Health Professions Medicine & Pharmacology Physics Public Health Social Sciences Other

Published : 6 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 6 Documents
Search

 1 
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.
Kepatuhan terhadap Pedoman Aktivitas Fisik WHO pada Anak Usia Dini : Evaluasi dengan Metode Machine learning Ramdhan, Akhmad Faizal; Suherman, Adang; Jajat; Sultoni, Kuston; Damayanti, Imas; Ruhayati, Yati
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.v10i1.3490

Abstract

Studi ini berupaya untuk mengevaluasi dan mengukur tingkat aktivitas fisik anak usia dini sejalan dengan pedoman Organisasi Kesehatan Dunia (WHO), menggunakan teknik klasifikasi machine learning pada data yang diperoleh dari kuesioner. Menyadari pentingnya aktivitas fisik di tahun-tahun formatif, penelitian ini bertujuan untuk menilai kepatuhan terhadap ambang batas aktivitas yang direkomendasikan WHO pada anak usia dini. Metodologi ini mengintegrasikan kuesioner komprehensif yang mengungkap beragam aspek pola aktivitas fisik anak usia dini, yang mencakup durasi, intensitas, dan jenis aktivitas yang dilakukan dalam berbagai situasi. Sebanyak 99 orang tua siswa melaporkan aktivitas keseharian anak mereka yang berusia 4 sampai 5 tahun (M = 4,59±0,41). Dengan memanfaatkan model klasifikasi algoritma machine learning decision tree, penelitian ini memproses data yang dikumpulkan untuk membedakan pola dan mengklasifikasikan tingkat aktivitas berdasarkan kriteria WHO. Hasilnya menunjukkan, indikator waktu aktivitas, waktu tidur dan waktu bermain menjadi indikator penentu decision tree dalam mengklasifikasi kepatuhan anak usia dini terhadap rekomendasi aktivitas fisik WHO. Lebih lanjut, machine learning decision tree sangat efektif dalam mengevaluasi dan mengklasifikasikan kepatuhan aktivitas fisik anak usia dini dengan performa akurasi 90%. Efektivitas pendekatan machine learning decision tree dalam mengevaluasi dan mengkategorikan tingkat aktivitas fisik anak usia dini secara akurat, menyoroti bidang-bidang potensial untuk intervensi dan strategi yang ditargetkan untuk meningkatkan kepatuhan terhadap aktivitas fisik yang direkomendasikan oleh WHO. Metodologi ini menawarkan instrumen yang menjanjikan bagi para profesional kesehatan, pembuat kebijakan, dan pendidik untuk lebih memahami dan mengatasi perilaku aktivitas fisik anak usia dini, sehingga berkontribusi terhadap promosi gaya hidup sehat sejak usia dini.
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.
Physical Condition and Concentration on Pencak Silat Athletes Salsaputri, Della; Sultoni, Kuston; Jajat; Ruhayati, Yati; Nur Ajid, Oktoviana
Journal of Sport Science and Fitness Vol. 11 No. 1 (2025): Journal of Sport Science and Fitness
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jssf.v11i1.25638

Abstract

Introduction: Physical condition and concentration are key factors influencing the performance of pencak silat athletes in both fighter and artistic categories. However, the relationship between these two aspects has rarely been explored specifically in this sport. Objectives: This study aimed to compare physical condition and concentration levels between athletes in the fighter and artistic categories, and to examine the relationship between the. Method: A cross-sectional design was used involving 36 athletes (18 fighter and 18 artistic). Physical condition was assessed through power, speed, agility, and endurance tests, while concentration was measured using the Grid Concentration Test (GCT). Data were analyzed using independent samples t-tests and Pearson correlation. Result: The results showed that fighter athletes had significantly higher power, speed, and endurance (p < 0.05), whereas artistic athletes had significantly better agility (p < 0.05). No significant differences in concentration levels were found between the two groups (p > 0.05). Additionally, there was no significant correlation between physical condition and concentration in either group (p > 0.05). Conclusion: These findings suggest that physical and cognitive aspects may develop independently. Therefore, training programs should incorporate both components to enhance overall athlete performance.
The Relationship Between Diet, Physical Activity, Rest Patterns and Fitness Najla Salsabila; Herman Subarjah; Jajat
Journal of Physical Education Health and Sport Vol. 12 No. 2 (2025)
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jpehs.v12i2.39316

Abstract

Healthy lifestyle habits during adolescence, especially among junior high school students, are an important stage in supporting long-term physical development and fitness. This study aims to determine the relationship between diet, physical activity, and rest patterns with fitness focused on the cardiorespiratory endurance of junior high school students. The study used a cross-sectional design and involved 60 eighth and ninth grade students at junior high school 1 Panggarangan, Lebak Regency, Banten. Data were collected using the AFHC questionnaire for dietary patterns, the IPAQ for physical activity, the PSQI for sleep patterns, and the Beep Test to measure fitness. Data analysis includes descriptive statistics, Spearman’s correlation, and multiple linear regression. The results of the study show that diet is related to physical activity, where students with healthier eating habits tend to be more active. However, no relationship was found between diet, physical activity, and sleep patterns and fitness levels. Regression analysis also shows that these three variables do not contribute significantly to variations in student fitness. These findings indicate that cardiorespiratory fitness is influenced by factors other than the daily habits measured in this study. This study provides an initial overview of the lifestyle and fitness conditions of junior high school students, and can serve as a basis for schools and related parties to develop fitness improvement programs.
The Effect of Physical Activity on Mood Ayu Ria Azizah; Mohammad Zaky; Jajat
Journal of Physical Education Health and Sport Vol. 12 No. 2 (2025)
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jpehs.v12i2.40543

Abstract

Physical activity is considered to affect mood, but this relationship is not always consistent and may depend on the context of the activity and individual characteristics. This study aims to analyze the effect of physical activity on mood among members of a recreational sports community using a cross-sectional study design involving 52 respondents selected through purposive sampling. Physical activity was measured using the International Physical Activity Questionnaire (IPAQ) and grouped into three type of activities (running, trail run, and cycling), while mood was measured using the Brunel Mood Scale (BRUMS). Data analysis was performed using One-Way ANOVA test, which showed that there were no significant differences in physical activity levels between activity groups (F = 2.502; p = 0.092) or in mood conditions between physical activity groups (F = 2.006; p = 0.145). These findings indicate that physical activity does not have a significant effect on mood in the recreational sports community in this study, suggesting taht the relationship between physical activity and mood is complex and may be influenced by other factors such as intensity, consistency, social context, and unmeasured psychological variables.
Co-Authors Adang Suherman Ayu Ria Azizah Herman Subarjah Imas Damayanti Mohammad Zaky Najla Salsabila Nur Ajid, Oktoviana Nur Indri Rahayu Raihan Rizki Ramadhan Ramdhan, Akhmad Faizal Ruhayati, Yati Salsaputri, Della Saputra, Diki Saputra Sultoni, Kuston Wandani, Syifa Yati Ruhayati