A MACHINE LEARNING APPROACH TO PREDICTING PHYSICAL ACTIVITY LEVELS IN ADOLESCENTS

Desvy Rahma Putri Mahendra, - (2023) A MACHINE LEARNING APPROACH TO PREDICTING PHYSICAL ACTIVITY LEVELS IN ADOLESCENTS. S1 thesis, Universitas Pendidikan Indonesia.

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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 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

Item Type:	Thesis (S1)
Additional Information:	https://scholar.google.com/citations?view_op=list_works&hl=en&hl=en&user=xYMUWVYAAAAJ ID SINTA Dosen Pembimbing : Jajat : 6002301 Imas Damayanti : 6137276
Uncontrolled Keywords:	accelerometer; physical activity; descision tree; SVM
Subjects:	G Geography. Anthropology. Recreation > GV Recreation Leisure L Education > L Education (General) Q Science > QA Mathematics > QA76 Computer software
Divisions:	Fakultas Pendidikan Olahraga dan Kesehatan > Jurusan Pendidikan Kesehatan dan Rekreasi > Program Studi IKOR
Depositing User:	Desvy Rahma Putri Mahendra
Date Deposited:	13 Feb 2024 07:15
Last Modified:	13 Feb 2024 07:15
URI:	http://repository.upi.edu/id/eprint/115072

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