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All Journal Jurnal Matematika CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Sains Matematika dan Statistika Science and Technology Indonesia Jurnal Penelitian Sains BAREKENG: Jurnal Ilmu Matematika dan Terapan Teorema: Teori dan Riset Matematika QARDHUL HASAN: MEDIA PENGABDIAN KEPADA MASYARAKAT KOMPUTEK Indonesian Journal of Electrical Engineering and Computer Science Inferensi Diseminasi : Jurnal Pengabdian kepada Masyarakat Prima Abdika: Jurnal Pengabdian Masyarakat Jurnal Teknologi Jurnal Pepadu COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi Jurnal Matematika Integratif
Endro Setyo Cahyono, Endro Setyo
Jurusan Matematika, Fakultas MIPA, Universitas Sriwijaya
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Perbandingan Kinerja Algoritma Random Forest dan Gradient Boosting dalam Klasifikasi Risiko Stunting pada Balita Diah Suci Ramadhani; Marisa -; Anita Desiani; Novi Rustiana Dewi; Bambang Suprihatin; Endro Setyo Cahyono; Lucky Indra Kesuma
Jurnal Teknologi Vol 26, No 2 (2026): Agustus 2026
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/teknologi.v26i2.9285

Abstract

Stunting in toddlers is a chronic nutritional problem that can impede physical growth and cognitive development; therefore, early detection is crucial to prevent long-term impacts. This study compared Random Forest and Gradient Boosting algorithms for classifying stunting risks in toddlers. The comparison of both algorithms was conducted to determine the model with better performance in classifying stunting status into four categories: severely stunted, stunted, normal, and tall. Testing was performed using the Percentage Split method (80% training data and 20% testing data) and 10-Fold Cross Validation. Model performance was measured based on accuracy, precision, and recall metrics. The results showed that the Random Forest algorithm produced better and more consistent performance compared to Gradient Boosting across both testing methods. In the Percentage Split method, Random Forest achieved an accuracy of 95.43%, meaning the model was able to accurately predict stunting status in a single test data split, whereas Gradient Boosting only achieved 85.68%, indicating a higher prediction error rate. In the 10-Fold Cross Validation method, Random Forest maintained an accuracy of 94.89%, meaning the model remained consistent despite being repeatedly tested using varied data subsets, while Gradient Boosting decreased to 77.47%, indicating that the model was unstable and sensitive to data variations. Additionally, Random Forest demonstrated stable precision and recall values above 90% across all stunting categories, particularly for the normal and tall categories. In conclusion, the Random Forest algorithm is more effective in classifying stunting risks in toddlers than the Gradient Boosting algorithm.
Co-Authors Agung Alamsyah Alamsyah, Agung Alfensi Faruk Ali Amran Anita Desiani Armalia, Divasanda Asri Kuntari Asri Kuntari, Asri Bambang Suprihatin Bambang Suprihatin Des Alwine Zayanti, Des Alwine Dewi, Deshinta Arrova Diah Suci Ramadhani Dian Cahyawati Eka Susanti Eka Susanti Eka susanti Elisa Nurnawati Evi Yuliza, Evi Fitri Maya Puspita Fitri Maya Puspita Gio Villando Indrawati Indrawati Indrawati Lucky Indra Kesuma Marisa - Muhammad 'Aqil Muhammad Alifian Nuriman Muji Gunarto Nadya Amalia Yulianti Ning Eliyati Novi Rustiana Dewi Oki Dwipurwani Pebriyanti Simanjuntak Permata, Anggun Pratiwi, Ananda Putra Bahtera Jaya Bangun, Putra Bahtera Jaya Putra BJ Bangun, Putra BJ Rima Kesuma Robinson Sitepu Rufi'i Sarjani, Ita Sisca Octarina Siti Suzlin Supadi Yanita, Nurul Fadhila