Algoritma Random Forest, Decision Tree dan XGboost Untuk Klasifikasi Stunting Pada Balita - Dhika Malita; KARTIKA IMAM SANTOSO; ANDRI TRIYONO; EKO SUPRIYADI; AGUS SUSILO NUGROHO; Widodo, Edi (Jurnal Transformatika, 2025)
At toddler age, they require special attention, as this is the period when the brain develops up to 80%. Stunting is a form of chronic malnutrition that affects a child’s growth and development. According to WHO standards, it is characterised by height that is below or lower than that of peers. This condition has negative impacts on cognitive development and overall health. Identifying toddlers at risk of stunting early is crucial to minimising the negative effects that could impact their quality of life in the future. Traditional methods are less effective in predicting stunting because they often overlook the complex factors that influence an infant’s nutritional status. This study aims to compare three algorithms and identify the most effective one for analysing infant stunting data. The method used involves comparing the results of the Random Forest, Decision Tree, and Extreme Gradient Boost (XGBoost) algorithms. The results obtained show that the Random Forest algorithm achieved the highest accuracy at 99.72%, Extreme Gradient Boost (XGBoost) at 99.58%, and Decision Tree at 98.87%.