Random Forest Algorithm for Toddler Nutritional Status Classification Website

  • Maylia Fatmawati Universitas PGRI Semarang
  • Bambang Agus Herlambang Universitas PGRI Semarang
  • Noora Qotrun Nada Universitas PGRI Semarang
Keywords: Random Forest, Toddler Nutritional Interventions, Toddler Nutritional Status, Website

Abstract

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.

Downloads

Download data is not yet available.

References

S. Anwar, E. Winarti, and S. Sunardi, "Systematic Review of Risk Factors, Causes, and Impacts of Stunting in Children," Journal of Health Sciences, vol. 11, no. 1, pp. 88-94, 2022.

O. Martony, "Stunting in Indonesia: Challenges and Solutions in the Modern Era," Journal of Telenursing (JOTING), vol. 5, no. 2, pp. 1734-1745, 2023.

M. Mukhsin, "The Role of Information and Communication Technology in Implementing Village Information Systems for the Publication of Village Information in the Globalization Era," Teknokom, vol. 3, no. 1, pp. 7-15, 2020.

M. S. Haris, M. Anshori, and A. Khudori, "Prediction of Stunting Prevalence in East Java Province with Random Forest Algorithm," Journal of Informatics Engineering (Jutif), vol. 4, no. 1, pp. 11-13, 2023.

M. R. A. Ariyadi, S. Lestanti, and S. Kirom, "Classification of Stunted Toddlers Using Random Forest Classifier in Blitar District," JATI (Journal of Informatics Engineering Students), vol. 7, no. 6, pp. 3846-3851, 2023.

P. Handayani, A. F. Charis, and H. Harliana, "Machine Learning Classification of Toddler Nutritional Status Using Random Forest Algorithm," KLIK: Scientific Review of Informatics and Computer, vol. 4, no. 6, pp. 3064-3072, 2024.

E. N. Candra, I. Cholissodin, and R. C. Wihandika, "Classification of Toddler Nutritional Status Using Random Forest Optimization Method with Genetic Algorithm (Case Study: Cakru Public Health Center)," Journal of Information Technology and Computer Science Development, vol. 6, no. 5, pp. 2188-2197, 2022.

O. Adiputra and E. Setiawan, "Classification of Malicious URLs Using Improved Random Forest and Web-Based Random Forest Algorithm," Sains dan Informatika: Research of Science And Informatic, vol. 9, no. 1, pp. 8-14, 2023.

Mansourifar, Hadi; SHI, Weidong. Deep synthetic minority over- sampling technique. arXiv preprint arXiv:2003.09788, 2020.

De Zarzà, Irene; De Curtò, Joachim; Calafate, Carlos T. Area Estimation Of Forest Fires using TabNet with Transformers. Procedia Computer Science, 2023, 225: 553-563.

M. N. Arifin and D. Siahaan, "Structural and Semantic Similarity Measurement of UML Use Case Diagram," Lontar Komputer: Scientific Journal of Information Technology, vol. 11, no. 2, p. 88, 2020.

R. Gustriansyah, N. Suhandi, S. Puspasari, and A. Sanmorino, “Machine Learning Method to Predict the Toddlers’ Nutritional Status”, INFOTEL, vol. 16, no. 1, pp. 32-43, Jan. 2024.

I. Rahmi, Y. Wulandari, H. Yozza, and M. Syafwan, “Classification Of Toddler’s Nutritional Status Using The Rough Set Algorithm”, Barekeng: J. Math. & App., vol. 17, no. 3, pp. 1483-1494, Sep. 2023.

M. Ula, A. F. Ulva, M. Mauliza, M. A. Ali, and Y. R. Said, “Application Of Machine Learning In Determining The Classification Of Children’s Nutrition With Decision Tree”, J. Tek. Inform. (JUTIF), vol. 3, no. 5, pp. 1457-1465, Sep. 2022.

M. G. Daffa and P. H. Gunawan, "Stunting Classification Analysis for Toddlers in Bojongsoang: A Data-Driven Approach," 2024 2nd International Conference on Software Engineering and Information Technology (ICoSEIT), Bandung, Indonesia, 2024, pp. 42-46, doi: 10.1109/ICoSEIT60086.2024.10497515.

Published
2024-11-12
How to Cite
[1]
M. Fatmawati, B. Herlambang, and N. Nada, “Random Forest Algorithm for Toddler Nutritional Status Classification Website”, JAIC, vol. 8, no. 2, pp. 428-433, Nov. 2024.
Section
Articles