Parameter Testing on Random Forest Algorithm for Stunting Prediction
DOI:
10.33395/sinkron.v9i1.14264Keywords:
Machine Learning, Random Forest, n_estimators, max_depth, SMOTE, StuntingAbstract
Stunting is a significant public health problem, especially in developing countries like Indonesia. It is often caused by chronic malnutrition in the first 1,000 days of life, which can impact a child's physical growth and cognitive development. To find risk factors and find effective solutions, data analysis was conducted by utilising machine learning to predict stunting. This research uses the Random Forest algorithm with a focus on setting parameters such as n_estimators, max_depth, and the number of features to optimise model efficiency and accuracy. Using the 2023 Indonesian Health Survey data consisting of 25,800 data, this study managed to get the highest accuracy of 91.65% by a combination of Random Forest with parameter settings n_estimators 200, max_depth 30, and Synthetic Minority Oversampling Technique (SMOTE). Despite the high accuracy results, there are limitations such as potential noise coming from synthetic data from SMOTE and the limited number of features analysed. It is hoped that this research can contribute to the field of machine learning model development that is practically used to predict stunting, and support the government's efforts to reduce the stunting prevalence rate to 14% as targeted. This model also provides strategic insights for policy makers to design more effective data-driven interventions, which can help in decision making.
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Copyright (c) 2024 Ahmad Hasan Mubarok, Pujiono, Dicky Setiawan, Duta Firdaus Wicaksono, Eti Rimawati
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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