Random Forest Algorithm for Toddler Nutritional Status Classification Website
Accurate data processing is essential for classifying toddler nutritional status on a website platform. The Random Forest algorithm is particularly effective in this context due to its ability to manage large datasets and mitigate overfitting. This study leverages Flask as the web framework to ensur...
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| Format: | Article |
| Language: | English |
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Politeknik Negeri Batam
2024-11-01
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| Series: | Journal of Applied Informatics and Computing |
| Subjects: | |
| Online Access: | https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/8463 |
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| author | Maylia Fatmawati Bambang Agus Herlambang Noora Qotrun Nada |
| author_facet | Maylia Fatmawati Bambang Agus Herlambang Noora Qotrun Nada |
| author_sort | Maylia Fatmawati |
| collection | DOAJ |
| description | Accurate data processing is essential for classifying toddler nutritional status on a website platform. The Random Forest algorithm is particularly effective in this context due to its ability to manage large datasets and mitigate overfitting. This study leverages Flask as the web framework to ensure responsiveness and adaptability, optimizing the data processing experience for users. Using secondary data comprising 120,999 records, the research aims to answer: "What factors affect the accuracy of the Random Forest model in classifying toddler nutritional status?" Model evaluation yielded excellent performance metrics, with accuracy, precision, recall, and F1-score values of 99.91%, 100%, 100%, and 100%, respectively. These results highlight the informative attributes in the dataset, such as age, gender, and height, that enhance classification accuracy. The Flask-based website enables users, such as healthcare professionals and policymakers, to input essential data points and receive instant classification results, thereby supporting prompt and informed responses to nutritional health issues. This study confirms that the Random Forest algorithm, combined with an intuitive web interface, effectively classifies toddler nutritional status with high accuracy. |
| format | Article |
| id | doaj-art-4e2df60d8ad942b78e96f6227e8e516c |
| institution | OA Journals |
| issn | 2548-6861 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Politeknik Negeri Batam |
| record_format | Article |
| series | Journal of Applied Informatics and Computing |
| spelling | doaj-art-4e2df60d8ad942b78e96f6227e8e516c2025-08-20T02:20:49ZengPoliteknik Negeri BatamJournal of Applied Informatics and Computing2548-68612024-11-018242843310.30871/jaic.v8i2.84638463Random Forest Algorithm for Toddler Nutritional Status Classification WebsiteMaylia Fatmawati0Bambang Agus Herlambang1Noora Qotrun Nada2Universitas PGRI SemarangUniversitas PGRI SemarangUniversitas PGRI SemarangAccurate data processing is essential for classifying toddler nutritional status on a website platform. The Random Forest algorithm is particularly effective in this context due to its ability to manage large datasets and mitigate overfitting. This study leverages Flask as the web framework to ensure responsiveness and adaptability, optimizing the data processing experience for users. Using secondary data comprising 120,999 records, the research aims to answer: "What factors affect the accuracy of the Random Forest model in classifying toddler nutritional status?" Model evaluation yielded excellent performance metrics, with accuracy, precision, recall, and F1-score values of 99.91%, 100%, 100%, and 100%, respectively. These results highlight the informative attributes in the dataset, such as age, gender, and height, that enhance classification accuracy. The Flask-based website enables users, such as healthcare professionals and policymakers, to input essential data points and receive instant classification results, thereby supporting prompt and informed responses to nutritional health issues. This study confirms that the Random Forest algorithm, combined with an intuitive web interface, effectively classifies toddler nutritional status with high accuracy.https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/8463random foresttoddler nutritional interventionstoddler nutritional statuswebsite |
| spellingShingle | Maylia Fatmawati Bambang Agus Herlambang Noora Qotrun Nada Random Forest Algorithm for Toddler Nutritional Status Classification Website Journal of Applied Informatics and Computing random forest toddler nutritional interventions toddler nutritional status website |
| title | Random Forest Algorithm for Toddler Nutritional Status Classification Website |
| title_full | Random Forest Algorithm for Toddler Nutritional Status Classification Website |
| title_fullStr | Random Forest Algorithm for Toddler Nutritional Status Classification Website |
| title_full_unstemmed | Random Forest Algorithm for Toddler Nutritional Status Classification Website |
| title_short | Random Forest Algorithm for Toddler Nutritional Status Classification Website |
| title_sort | random forest algorithm for toddler nutritional status classification website |
| topic | random forest toddler nutritional interventions toddler nutritional status website |
| url | https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/8463 |
| work_keys_str_mv | AT mayliafatmawati randomforestalgorithmfortoddlernutritionalstatusclassificationwebsite AT bambangagusherlambang randomforestalgorithmfortoddlernutritionalstatusclassificationwebsite AT nooraqotrunnada randomforestalgorithmfortoddlernutritionalstatusclassificationwebsite |