Research Article
Naive Bayes Classifier (NBC) Application on the Nutritional Status of Adolescents in Medan
@INPROCEEDINGS{10.4108/eai.20-10-2022.2328884, author={Tyas Permatasari and Yatty Destani Sandy and Caca Pratiwi and Kanaya Yori Damanik and Agnes Irene Silitonga}, title={Naive Bayes Classifier (NBC) Application on the Nutritional Status of Adolescents in Medan}, proceedings={Proceedings of the 4th Annual Conference of Engineering and Implementation on Vocational Education, ACEIVE 2022, 20 October 2022, Medan, North Sumatra, Indonesia}, publisher={EAI}, proceedings_a={ACEIVE}, year={2023}, month={5}, keywords={adolescent nutritional status naive bayes machine learning}, doi={10.4108/eai.20-10-2022.2328884} }- Tyas Permatasari
Yatty Destani Sandy
Caca Pratiwi
Kanaya Yori Damanik
Agnes Irene Silitonga
Year: 2023
Naive Bayes Classifier (NBC) Application on the Nutritional Status of Adolescents in Medan
ACEIVE
EAI
DOI: 10.4108/eai.20-10-2022.2328884
Abstract
Adolescent nutrition problems in Indonesia are currently faced with three nutritional burdens (the triple burden), namely stunting, obesity, and micronutrient deficiencies. An unhealthy diet and a sedentary lifestyle are factors that cause nutritional problems in adolescents. Information technology is developing very rapidly and significantly. In the midst of the current COVID-19 pandemic situation, various digital technology innovations have emerged, especially in the health sector. The research will be conducted in junior high, high school, and university schools, and its implementation will start from April to August 2022. The sample was taken purposefully, with a total sample of 150 respondents. The research method used is cross-sectional in primary data collection for nutritional status. Furthermore, primary data has been collected through direct measurements and interviews using questionnaires. The data set will be classified and analysed using the naive Bayes method. The tools used in this study are Rapid Manner version 9.0.2. The tools will act to classify the cleaned data and assess the accuracy of the existing data. The results showed that the questionnaire or instrument had been validated by experts and that the respondents resembled the characteristics of the respondents. The average nutritional status in The percentage of adolescents who had an underweight nutritional status was 5.5%, 18.6% were overweight, and 22.8% were obese.