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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Jurnal Sistem Informasi dan Ilmu Komputer Jurnal Penelitian Teknologi Informasi dan Sains Bridge: Jurnal Publikasi Sistem Informasi dan Telekomunikasi Saturnus: Jurnal Teknologi dan Sistem Informasi Variable Research Journal Jurnal Sistem Informasi dan Ilmu Komputer
Nita Syahputri
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Social Sciences

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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 Ahmad Syah Lubis Alfina, Ommi Alif Azhar Amsyari Anggi Canita Simanjuntak Bayu Gunawan Bremi Br Ginting Della Puspita Dhoni Brilliant Dinda Ayu Alifia Enok Tuti Alawiah Fadhillah Azzahra Gusti Masari Pangaribuan Jamil Azhari Lystiowati purwopeni M. Safii Malina Nst, Wiwik Miranda Elisabet Sitanggang Muhairoh Indah Cahyani Muhammad Taufiq Rustam Nikita Br. Nababan Nurul Nazli Putrama Alkhairi Rera Zetira Rizky Hamdani Satrya Prima Raudini Shella Alivia Ahmad Siahaan Sri Panca Rani