Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Merkurius: Jurnal Riset Sistem Informasi dan Teknik Informatika
Muhamad Fikri
Universitas Muhammadiyah Ponorogo
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Klasifikasi Status Stunting Pada Anak Bawah Lima Tahun Menggunakan Extreme Gradient Boosting Muhamad Fikri
Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika Vol. 2 No. 4 (2024): Juli : Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/merkurius.v2i4.159

Abstract

Stunting is a condition of failure to thrive in children, in Indonesia it is still a serious problem with a fairly high prevalence. The government is trying to reduce stunting rates with various health programs, and early detection through routine measurements is very important. This research uses the Extreme Gradient Boosting (XGBoost) algorithm to classify stunting status in children under five years. This study uses a relevant dataset containing anthropometric information on children, such as gender, age, birth weight and length, current weight and length, and breastfeeding status. The research stages include dataset search, preprocessing, classification, evaluation, and implementation in a local web-based prediction program. The XGBoost algorithm was chosen because of its advantages in speed, scalability, and efficiency. After preprocessing and data sharing, the model was trained and tested, resulting in 86% accuracy, 89% precision, 95% recall, and 92% F1-score. Evaluation using the confusion matrix and classification report shows that this model is quite effective in classifying stunting status.
Co-Authors