Article Per Year (5 Year)
p-Index From 2021 - 2026
1.687
P-Index
This Author published in this journals
All Journal Jurnal Pseudocode Journal of Animation & Games Studies Sistemasi: Jurnal Sistem Informasi Jurnal Teknologi Informasi dan Terapan (J-TIT) Jurnal Abdi Insani Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Profesi Insinyur Indonesia Aktual : Jurnal Pengabdian Kepada Masyarakat Indonesian Journal of Computer Science and Engineering Dharmakayana: Journal of scientists, engineers, educators and scientific activists related to society development
Nurul Renaningtias, Nurul
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Public Health Social Sciences Transportation

Published : 11 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 11 Documents
Search

 1   2 
Child Stunting Risk Analysis through Machine Learning Models using XGBoost Algorithm Renaningtias, Nurul; Prihatiningrum, Atik; Hardiansyah, Hardiansyah; Setiawan, Yudi; Vatresia, Arie
Jurnal Teknologi Informasi dan Terapan Vol 12 No 2 (2025): December
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v12i2.452

Abstract

Stunting is a chronic nutritional disorder that significantly affects child growth, development, and the overall quality of future human resources. According to the 2024 Indonesian Nutritional Status Survey (SSGI), the prevalence of stunting remains high at 19.8%, equivalent to approximately 4.48 million children under five. Early detection of stunting risk is essential for timely and data-driven interventions. This study employed the CRISP-DM methodology, encompassing business understanding, data collection, preparation, modeling, and evaluation phases. The dataset was processed through cleaning, variable encoding, and stunting status classification based on WHO standards. An XGBoost-based predictive model was developed and evaluated using accuracy, precision, recall, and F1-score metrics. The model achieved 98% accuracy in predicting stunting risk. Feature importance analysis revealed that height is the most influential variable determining stunting risk.
Co-Authors Abdillah, Benito Jakob Arie Vatresia Atik Prihatiningrum, Atik Daratha, Novalio Endina Putri Purwandari Erlansari, S.T.,M.Eng, Aan Hardiansyah Hardiansyah Hidayat, Pradifta Irsyad Ibrahim, Tanimu Adam Kartika, Putri Khairul Amri Rosa, Muhammad Kurnia Anggriani, Kurnia Kusuma, Teguh Nata Nata Kusuma, Teguh Natalio, Jandri Nurhamdi, Yahya Masykur Putri Salsabila, Aprieza Putri, Tiara Eka Sri Wulandari Syafrudin Syafrudin Ulfah Anis Vicka Andini Vitus Dwi Yunianto Budi Ismadi Wahyuni Saragih, Destri Wijanarko, Andang Yudi Setiawan Yuwita, Fitri