Skripsi

INTENSITAS PENGGUNAAN GADGET DAN AKTIVITAS FISIK SEBAGAI RISIKO OBESITAS PADA ANAK SEKOLAH DASAR MENGGUNAKAN SUPERVISED MACHINE LEARNING K-NEAREST NEIGHBORS

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Childhood obesity is a major global public health problem with an increasing prevalence, including in Indonesia. This condition results from multifactorial interactions among genetic, behavioral, and environmental factors, such as high gadget use intensity and low levels of physical activity. This study aimed to analyze the relationship between gadget use intensity and physical activity with obesity risk among elementary school children, as well as to evaluate the accuracy of the Machine Learning algorithm K-Nearest Neighbors (KNN) in predicting obesity occurrence. This research employed an analytical observational design with a cross-sectional approach, involving 855 children aged 7–12 years from elementary schools in the Seberang Ilir and Seberang Ulu areas of Palembang City. Data were analyzed using binary logistic regression to determine the relationships among variables and the KNN algorithm through Orange Data Mining software for predictive evaluation. The results showed an obesity prevalence of 6.20%, with dominant factors including parental obesity history (OR=7.61; p

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Availability

Detail Information

Series Title

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Call Number

T1869242025

Publisher

Palembang : Prodi Pendidikan Kedokteran, Fakultas Kedokteran Universitas Sriwijaya., 2025

Collation

xix, 148 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

616.398 07

Content Type

Text

Media Type

unmediated

Carrier Type

other (computer)

Edition

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Subject(s)

Prodi Pendidikan Kedokteran
Obesitas--Kelebihan Berat Badan

Specific Detail Info

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Statement of Responsibility

MI

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