Spatial Prediction of Stunting Incidents Prevalence Using Support Vector Regression Method

  • Andi Widya Mufila Gaffar Universitas Muslim Indonesoa
  • Sugiarti Universitas Muslim Indonesia
  • Dewi Widyawati Universitas Muslim Indonesia
  • Andi Muhammad Kemai Arief Hidayat Paharuddin Universitas Muslim Indonesia
  • Andi Vania Anastasia Universitas Muslim Indonesia

Keywords: Stunting, Prediksi, Spasial, Machine Learning, Support Vector Machine

Abstract

Stunting in toddlers is a major nutritional problem faced by Indonesia, with a high incidence rate occurring in several provinces across the country. This nutritional issue can occur at any age, starting from the prenatal stage, infancy, childhood, adolescence, adulthood, and even in the elderly. To reduce the prevalence of stunting in affected provinces, prevention efforts are essential, including predicting the spread of stunting incidents in each region. Therefore, this research conducted spatial prediction of the prevalence rate of stunting incidents using Machine Learning, specifically Support Vector Machine based Regression. The results of this study produced a prediction model with an RMSE (Root Mean Square Error) value of 0.008689303 and a multiple correlation coefficient of 0.65912721. Based on these findings, the predictive model utilized demonstrated satisfactory performance in predicting the prevalence rate of stunting incidents in each area

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Author Biography

Andi Widya Mufila Gaffar, Universitas Muslim Indonesoa

 

 

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Published
2023-07-31
How to Cite
Gaffar, A. W. M., Sugiarti, Dewi Widyawati, Andi Muhammad Kemai Arief Hidayat Paharuddin, & Andi Vania Anastasia. (2023). Spatial Prediction of Stunting Incidents Prevalence Using Support Vector Regression Method. Indonesian Journal of Data and Science, 4(2), 71-77. https://doi.org/10.56705/ijodas.v4i2.68