Comparison of Machine Learning Algorithms for Predicting Stunting Prevalence in Indonesia

Authors

  • Moh. Asry Eka Pratama Information Systems Study Program, Faculty of Engineering. Tadulako University, Palu
  • Syaiful Hendra Informatics Engineering Study Program, Faculty of Engineering. Tadulako University, Palu https://orcid.org/0000-0002-0037-5895
  • Hajra Rasmita Ngemba Information Systems Study Program, Faculty of Engineering. Tadulako University, Palu https://orcid.org/0000-0001-8727-9267
  • Rosmala Nur Department of Public Health, Faculty of Public Health. Tadulako University, Palu
  • Ryfial Azhar Informatics Engineering Study Program, Faculty of Engineering. Tadulako University, Palu
  • Rahmah Laila Informatics Engineering Study Program, Faculty of Engineering. Tadulako University, Palu

DOI:

https://doi.org/10.32736/sisfokom.v13i2.2097

Keywords:

Stunting, Machine Learning, Stunting Prevalence, Regression Algorithm, Tableau

Abstract

Stunting is a serious public health problem, especially among under-fives, which can cause serious short- and long-term impacts. Efforts to tackle stunting in Indonesia involve national strategies and development priorities. Therefore, this study aims to compare the performance of machine learning regression algorithms in predicting stunting prevalence in Indonesia. The data collected is secondary data. The data collection was done carefully, taking explicit details regarding the source, scope, extent, and analysis of the dataset, and using a careful sampling methodology. The model evaluation results show that the Random Forest Regression algorithm has the best performance, with a success rate of 90.537%. The application of this model to the new dataset shows that East Nusa Tenggara province has the highest percentage of stunting at 31.85%, while Bali has the lowest percentage at 12.07%. Visualization of the dashboard using Tableau provides a clear picture of the distribution of stunting in Indonesia. In conclusion, this research contributes to the development of science, especially in the field of machine learning and public health, and provides policy recommendations for tackling stunting in Indonesia.

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Published

2024-06-10

Issue

Vol. 13 No. 2 (2024): JULY

Section

Articles

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