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All Journal METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi J-SAKTI (Jurnal Sains Komputer dan Informatika) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Pengabdian Pada Masyarakat METHABDI Majalah Ilmiah METHODA TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Alfina, Ommi
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Religion Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Immunology & microbiology Languange, Linguistic, Communication & Media Medicine & Pharmacology Physics Public Health

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Hybrid Feature Selection dan Ensemble Learning untuk Klasifikasi Risiko Stunting Anak di Indonesia Alfina, Ommi; Nita Syahputri; M. Safii; Muhammad Taufiq Rustam
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 1 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No1.pp107-111

Abstract

Stunting is a chronic nutritional problem that remains a major public health issue in Indonesia. This study aims to develop a classification model for stunting risk in children using a combination of hybrid feature selection and ensemble learning methods. The dataset used is derived from socio-economic and health data obtained from the Central Statistics Agency and open datasets. The research method includes data preprocessing, feature selection, model development using Random Forest and Gradient Boosting combined with a Voting Classifier, and evaluation using accuracy, precision, recall, F1-score, and ROC-AUC metrics. The results show that the proposed model achieves high performance with accuracy reaching 98% and ROC-AUC close to 1. The hybrid feature selection successfully improves model efficiency by selecting relevant features. This study demonstrates that the integration of feature selection and ensemble learning can produce an accurate and interpretable model for early detection of stunting risk.
Co-Authors Ambarita, Wisesa Panca Praja Damanik Ambiyar, Ambiyar Ananda Pratama Astuti, Ermayanti Ginting, Erwin Hutasuhut, Dedek Indra Gunawan Ikorasaki, Frans Jamaluddin Khairul Ummi, Khairul Lahilote, Abqoriy Hisan Lubis, Nur Azizah M. Safii Muhammad Taufiq Rustam Nita Syahputri Puspita, Dahlia Ramadhania, Dwindah RUSTAM, MUHAMMAD TAUFIQ Safii, M. SIREGAR, ELIDA TUTI Syahputri, Nita Unung Verawardina, Unung Utomo, Tedi Zaharani, Hetty