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All Journal Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika)
Panigoro, Buyung
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Computer Science & IT Decision Sciences, Operations Research & Management

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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
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v10i2.904

Abstract

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.
Co-Authors Mula Agung Barata Nur Mahmudah