Klasifikasi Stunting Balita menggunakan Metode Ensemble Learning dan Random Forest
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Abstract
Stunting is a long-term condition that describes nutritional deficiencies that affect children's growth and development from an early age, especially linear growth. Examination of the stunting status of toddlers in Indonesia, especially at the Karanganyar Community Health Center, still uses book calculations so errors are still found in the use of formulas which result in inaccuracies in the classification of stunting. Efforts to improve research results were carried out using the Random Forest algorithm which was enhanced with ensemble methods such as the Bagging and Boosting methods to classify stunting data. The aim of this research is to find out which technique will produce the best and most accurate accuracy. The Ensemble Boosting techniques used are XGBoost and Gradient Boosting. This research uses a dataset from the Karanganyar Health Center, Semarang City with a total of 2000 data records. The test results produced the highest accuracy algorithm, namely the Random Forest + Bagging algorithm which obtained accuracy results of 98.25%. Based on the analysis results obtained, the Bagging and Boosting methods can accurately predict stunting data.
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