Comparison of Ordinal Logistic Regression and Artificial Neural Network in Stunting Prevalence Classification

May Risnawati, M. Fathurahman, Surya Prangga

Abstract


The prevalence of under-five stunting in one of the crucial health problems in Indonesia. Stunting is a growth and development disorder in children due to chronic malnutrition and repeat infections that can have a negative impact on children’s physical and cognitive development. This study aims to analyse the accuracy of the classification of the prevalence of stunting on regencies/cities in Indonesia, in 2022 using two methods, namely Ordinal Logistic Regression (OLR) and Artificial Neural Network (ANN). OLR is development of logistic regression applied to response variables with more the two categories that have levels or ranks, while ANN is a method that mimics the function of the biological nervous system and is designed for complex information processing. This study used two proportions of data splitting namely 80:20 and 90:10. Each method produce two models, OLR 1 and OLR 2 for the OLR method, and ANN 1 and ANN 2 for the ANN method. The results show that the ANN 1 model with 80:20 data proportion performs better than other models with an accuracy of 63.37%.

Keywords


ANN; classification; OLR; stunting.

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References


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DOI: http://dx.doi.org/10.12962%2Fj27213862.v8i3.22287

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