[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119935-en":3,"doc-seo-119935-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119935,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","A Machine Learning Approach to Predicting Physical Activity Levels in Adolescents","Technology’s progress has made daily activities easier while simultaneously contributing to reduced physical activity, which increases the likelihood of chronic diseases and global mortality. This research evaluates how machine learning can predict adolescents’ physical activity levels using accelerometer data from the ActiGraph GT3X. A semisupervised framework is applied with support vector machine and decision tree algorithms. For 61 adolescents aged 18–21 in West Java, the decision tree achieves 97.50% accuracy versus 92.5% for SVM via confusion-matrix analysis.","A Machine Learning Approach to Predicting Physical Activity Levels in Adolescents  \nDesvy Rahma Putri Mahendra 1A-E*, Jajat2A-D, Imas Damayanti3AD, Kuston Sultoni4D,  \nYati Ruhayati5D, Adang Suherman6DE, Nur Indri Rahayu7D  \n1-7Fakultas Pendidikan Olahraga dan Kesehatan, Universitas Pendidikan Indonesia, Bandung, Indonesia  \nABSTRACT  \nThe 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 semisupervised machine learning approach, using both the support vector machine and decision tree algorithms to make these predictions. The study sample consists of 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, it can be concluded that the decision tree algorithm outperforms the support vector machine algorithm in terms of accuracy. Further analyses involving different models are necessary to determine which algorithm offers the highest level of accuracy.  \nKeywords: accelerometer; physical activity; descision tree; SVM  \nCorresponding author:  \n*Desvy Rahma Putri Mahendra, Universitas Pendidikan Indonesia, Jl. Dr. Setiabudhi No.229, Bandung, West Java, Indonesia, [40154. ](40154. desvyrahma@upi.edu)[desvyrahma@upi.edu](40154. desvyrahma@upi.edu)  \nArticle History:  \nReceived: September 25, 2023  \nAccepted after revision: October 23, 2023  \nFirst Published Online: October 30, 2023  \nAuthors’ contribution:  \nA) Conception and design of the study;  \nB) Acquisition of data;  \nC) Analysis and interpretation of data;  \nD) Manuscript preparation;  \nE) Obtaining funding.  \nCite this article:  \nMahendra, D. R. P., Jajat, Damayanti, I., Sultoni, K., Ruhayati, Y., Suherman, A., & Rahayu, N. I. (2023) . A Machine Learning Approach to Predicting Physical Activity Levels in Adolescents. Indonesian Journal of Sport Management, 3(2), 261-272.  \n[https://doi.org/10.31949/ijsm.v3i1.7145](https://doi.org/10.31949/ijsm.v3i1.7145)  \nINTRODUCTION  \nIn the modern era, technology continues to advance, yielding both positive and negative consequences. Technological progress has positively impacted society by enhancing the ease, convenience, and speed with which individuals can perform various tasks. However, it has also led to negative outcomes, including a decline in physical activity among individuals, as they are increasingly reliant on the conveniences provided by modern technology (Jeckzen et al., 2019) . This trend is evident in rural areas, where farmers are more inclined to use tractors for ploughing fields instead of traditional buffalo ploughing methods. Similarly, in urban environments, contemporary children often prefer electronic gadgets over traditional outdoor games with their peers (Sulastri & Sonyo Rini, 2022) .  \nThe extensive use of technology in our modern society has made many work tasks more efficient (Pramono et al., 2014) . However, this advancement in technology has also led to reduced levels of physical activity (Suherman et al., 2021) . Physical activity plays a vital role in maintaining overall health and well-being, regardless of  \nage, from earl","cbCaijlxgxKa1k3p","https://ap.wps.com/l/cbCaijlxgxKa1k3p","pdf",452830,1,12,"English","en",105,"# Abstract\n# Introduction\n## Technology and Physical Activity\n## Assessing Physical Activity Methods\n## Accelerometer Capabilities and Limitations","[{\"question\":\"What is the main objective of the study?\",\"answer\":\"To assess the effectiveness of machine learning for predicting physical activity levels in adolescents using accelerometer data.\"},{\"question\":\"Which data source and sensor type are used for the predictions?\",\"answer\":\"The study uses accelerometer data specifically collected with the ActiGraph GT3X.\"},{\"question\":\"How do the decision tree and SVM models compare in accuracy?\",\"answer\":\"The decision tree model shows 97.50% accuracy, outperforming the SVM model, which reaches 92.5% based on confusion-matrix performance analysis.\"}]","A Machine Learning Approach to Predicting Physical Activity Levels in Adolescents | PDF",1785727075,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-machine-learning-approach-to-predicting-physical-activity-levels-in-adolescents","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-machine-learning-approach-to-predicting-physical-activity-levels-in-adolescents/119935/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main objective of the study?","Question",{"text":75,"@type":76},"To assess the effectiveness of machine learning for predicting physical activity levels in adolescents using accelerometer data.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which data source and sensor type are used for the predictions?",{"text":80,"@type":76},"The study uses accelerometer data specifically collected with the ActiGraph GT3X.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the decision tree and SVM models compare in accuracy?",{"text":84,"@type":76},"The decision tree model shows 97.50% accuracy, outperforming the SVM model, which reaches 92.5% based on confusion-matrix performance analysis.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]