Multiclass classification of toddler nutritional status using support vector machine: A case study of community health centers in Bangkalan, Indonesia
Monitoring child development is vital in Indonesia due to its large child population and varying socio-economic and geographical conditions. Malnutrition adversely affects children's growth and development, with ongoing challenges in remote areas despite government efforts. This study addresses...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2024-01-01
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| Series: | BIO Web of Conferences |
| Online Access: | https://www.bio-conferences.org/articles/bioconf/pdf/2024/65/bioconf_btmic2024_01082.pdf |
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| author | Syakur Muhammad Ali Putra Adz Dzikry Pradana Rochman Eka Mala Sari Mufarrohah Fifin Ayu Husni Asmara Yuli Panca Rachmad Aeri |
| author_facet | Syakur Muhammad Ali Putra Adz Dzikry Pradana Rochman Eka Mala Sari Mufarrohah Fifin Ayu Husni Asmara Yuli Panca Rachmad Aeri |
| author_sort | Syakur Muhammad Ali |
| collection | DOAJ |
| description | Monitoring child development is vital in Indonesia due to its large child population and varying socio-economic and geographical conditions. Malnutrition adversely affects children's growth and development, with ongoing challenges in remote areas despite government efforts. This study addresses the need for accurate nutritional status classification to improve intervention strategies. This study applies the Support Vector Machine (SVM) classification method to analyze and classify nutritional status of toddlers using data from 473 samples collected from health centers in Bangkalan Regency. The classification includes categories such as Good Nutrition, Excess Nutrition, Obesity, and Risk of Excess Nutrition. The SVM model achieved an accuracy of 76% in predicting nutritional status. |
| format | Article |
| id | doaj-art-46cd2a00ba1e4871ae41377e3b2e5f79 |
| institution | Kabale University |
| issn | 2117-4458 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | BIO Web of Conferences |
| spelling | doaj-art-46cd2a00ba1e4871ae41377e3b2e5f792024-12-06T09:33:56ZengEDP SciencesBIO Web of Conferences2117-44582024-01-011460108210.1051/bioconf/202414601082bioconf_btmic2024_01082Multiclass classification of toddler nutritional status using support vector machine: A case study of community health centers in Bangkalan, IndonesiaSyakur Muhammad Ali0Putra Adz Dzikry Pradana1Rochman Eka Mala Sari2Mufarrohah Fifin Ayu3Husni4Asmara Yuli Panca5Rachmad Aeri6Departemen of Informatics, Faculty of Engineering, University of Trunojoyo MaduraDepartemen of Informatics, Faculty of Engineering, University of Trunojoyo MaduraDepartemen of Informatics, Faculty of Engineering, University of Trunojoyo MaduraDepartemen of Informatics, Faculty of Engineering, University of Trunojoyo MaduraDepartemen of Informatics, Faculty of Engineering, University of Trunojoyo MaduraFaculty of Engineering and Quantity Surveying, INTI International UniversityDepartemen of Informatics, Faculty of Engineering, University of Trunojoyo MaduraMonitoring child development is vital in Indonesia due to its large child population and varying socio-economic and geographical conditions. Malnutrition adversely affects children's growth and development, with ongoing challenges in remote areas despite government efforts. This study addresses the need for accurate nutritional status classification to improve intervention strategies. This study applies the Support Vector Machine (SVM) classification method to analyze and classify nutritional status of toddlers using data from 473 samples collected from health centers in Bangkalan Regency. The classification includes categories such as Good Nutrition, Excess Nutrition, Obesity, and Risk of Excess Nutrition. The SVM model achieved an accuracy of 76% in predicting nutritional status.https://www.bio-conferences.org/articles/bioconf/pdf/2024/65/bioconf_btmic2024_01082.pdf |
| spellingShingle | Syakur Muhammad Ali Putra Adz Dzikry Pradana Rochman Eka Mala Sari Mufarrohah Fifin Ayu Husni Asmara Yuli Panca Rachmad Aeri Multiclass classification of toddler nutritional status using support vector machine: A case study of community health centers in Bangkalan, Indonesia BIO Web of Conferences |
| title | Multiclass classification of toddler nutritional status using support vector machine: A case study of community health centers in Bangkalan, Indonesia |
| title_full | Multiclass classification of toddler nutritional status using support vector machine: A case study of community health centers in Bangkalan, Indonesia |
| title_fullStr | Multiclass classification of toddler nutritional status using support vector machine: A case study of community health centers in Bangkalan, Indonesia |
| title_full_unstemmed | Multiclass classification of toddler nutritional status using support vector machine: A case study of community health centers in Bangkalan, Indonesia |
| title_short | Multiclass classification of toddler nutritional status using support vector machine: A case study of community health centers in Bangkalan, Indonesia |
| title_sort | multiclass classification of toddler nutritional status using support vector machine a case study of community health centers in bangkalan indonesia |
| url | https://www.bio-conferences.org/articles/bioconf/pdf/2024/65/bioconf_btmic2024_01082.pdf |
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