Klasifikasi Stunting Pada Balita dengan Algoritma Random forest dan Support Vector machine

Panigoro, Buyung
Barata, Mula Agung
Mahmudah, Nur

Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika)
Vol 10, No 2 (2025): Edisi Agustus

Stunting is a health problem in the world, many factors cause stunting in toddlers, this study aims to compare the performance of the Random forest algorithm and Support Vector machine using a private dataset with a total of 618 toddler data in the Sumberharjo area in February, August 2023-2024. Adding a combination of smote techniques to handle unbalanced data and k-fold Cross-validation. The results showed the Random forest algorithm with a stable accuracy of 95.41% after reaching 94.35%. For the Support Vector machine algorithm, it achieved an accuracy of 81.45% after being smote to 83.06% and the recal decreased to 51.16%. Random forest is more recommended for classifying stunting in toddlers with stable results compared to Support Vector machines.