STUDI KASUS KLASIFIKASI STUNTING PADA BALITA DI KARYAJAYA SUMATERA SELATAN MENGGUNAKAN EXTREME GRADIENT BOOSTING DAN K-NEAREST NEIGHBORS - Dalam bentuk buku karya ilmiah
RAIHAN ABDURRAHMAN

Informasi Umum

Kode

25.04.1380

Klasifikasi

000 - General Works

Jenis

Karya Ilmiah - Skripsi (S1) - Reference

Subjek

Data Science

Dilihat

34 kali

Informasi Lainnya

Abstraksi

<strong>Stunting, a consequence of persistent malnutri- tion, severely hinders the physical and cognitive development of children under five years of age. In Indonesia, the incidence of stunting was 21.6% in 2022, presenting a significant public health challenge. This research seeks to create efficient machine learning (ML) models for identifying stunting status in chil- dren, utilizing a dataset of 3,000 physical measurements from the Karyajaya Health Center in South Sumatra. The study utilizes two classification algorithms: K-Nearest Neighbors (KNN) and Extreme Gradient Boosting (XGBoost). Data pre- processing encompassed feature selection, outlier elimination via the Interquartile Range (IQR) approach, and rectification of class imbalance through the Synthetic Minority Over- sampling Technique (SMOTE) and parameter optimization. The KNN model was set with five neighbors and distance-based weighting, resulting in an accuracy of 99.43% and an F1-score of 93.89%. The XGBoost model, optimized with a calibrated scale-pos-weight parameter, surpassed KNN by achieving an accuracy of 99.93% and an F1-score of 99.62%. These findings illustrate XGBoost’s exceptional capacity to manage intricate, unbalanced datasets and underscore its promise as a reliable instrument for early stunting identification. The application of XGBoost can assist policymakers and healthcare profes- sionals in delivering prompt interventions, therefore aiding the national objective of decreasing stunting prevalence to 14% by 2024. Subsequent study could improve model accuracy by integrating supplementary genetic and environmental variables across various demographic contexts.</strong><br />  

Koleksi & Sirkulasi

Tersedia 1 dari total 1 Koleksi

Pengarang

Nama	RAIHAN ABDURRAHMAN
Jenis	Perorangan
Penyunting	Putu Harry Gunawan
Penerjemah

Penerbit

Nama	Universitas Telkom, S1 Informatika
Kota	Bandung
Tahun	2025

Sirkulasi

Harga sewa	IDR 0,00
Denda harian	IDR 0,00
Jenis	Non-Sirkulasi

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