Article Per Year (5 Year)
p-Index From 2020 - 2025
1.347
P-Index
This Author published in this journals
All Journal Seruni - Seminar Riset Unggulan Nasional Inoformatika dan Komputer Jurnal Transformatika Joined Journal (Journal of Informatics Education) Transformasi Unnes Journal of Mathematics Jurnal Pengabdian Masyarakat Bhinneka Jurnal Kabar Masyarakat Jurnal Meteorologi dan Geofisika
Eko Supriyadi
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Physics Public Health Social Sciences Other

Published : 9 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Jurnal Transformatika

 1 
ALGORITMA RANDOM FOREST, DECISION TREE, DAN XGBOOST UNTUK KLASIFIKASI STUNTING PADA BALITA Dhika Malita; DHIKA MALITA PUSPITA ARUM; KARTIKA IMAM SANTOSO; ANDRI TRIYONO; EKO SUPRIYADI; AGUS SUSILO NUGROHO; Widodo, Edi
Jurnal Transformatika Vol. 23 No. 1 (2025): July 2025
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v23i1.12202

Abstract

At the age of toddlers, children need special attention because their brains develop around 80%. Stunting is a form of long-term nutritional deficiency that occurs during the growth and development of children, which are marked with height that is not appropriate or less compared to children their age based on the standard WHO. This condition can adversely affect the cognitive development and health of children. Identifying toddlers who are at risk of experiencing stunting at an early stage is very important to reduce the adverse effects that can affect their quality of life in the future. Traditional methods are less effective in predicting stunting because they often ignore the complex factors that affect the nutritional status of toddlers. This study aims to classify stunting toddlers using Random Forest, Decision Tree, and Extreme Gradient Boost (XGBOOST) algorithms. The results obtained showed that the accuracy of the Random Forest algorithm received the highest accuracy of 99.72 %, Extreme Gradient Boost (XGBOOST) at 99.58 %, and Decision Tree received 98 87 %accuracy.
Co-Authors Agus Susilo Nugroho Agus Wiyoso Akhmad Sholikhin Amalia Amalia Andri Triyono Arum, Dhika Malita Puspita Bambang Riyadi Basedt, Ngabdul Dhika Malita Dwi Indah Edi Widodo, Edi Emmanuel, Rheimanda Devin Erika Dwi Saputra Evi Nur Afidoh Farokhah, Mirna Vaul Ilham Kurniawan Imam Gozali Ivan Kurniawan Kartika Imam Santoso Lyan Audiah muzammil, muhammad Nugroho, Agus Susilo Rahmawan Bagus Trianto Rakiman Rakiman Riniasih, Wahyu RP Sigit Fiansyah Sari, Gigih Kenanga Scolastika Mariani Sisy Farah Adilah Slamet Setia Sugiman Sugiman Susi Afriyani susilo nugroho, agus Susilo Susilo Syahrul Afifudin Tanuri Tanuri Tasya Harsiami Trista Rima Setya Triyono, Andri Uci Tri Windarsih Ulfa, Fatma Nurmala