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Djoru, Ade Putra Tupu
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Pendekatan Machine Learning untuk Deteksi Stunting pada Balita Menggunakan K-Nearest Neighbors Djoru, Ade Putra Tupu; Yulianto, Sri
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 2 (2025): APRIL-JUNE 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v9i2.3436

Abstract

Stunting is a chronic nutritional problem affecting the physical growth and cognitive development of toddlers, especially in early childhood. This study employs the K-Nearest Neighbors (K-NN) algorithm to determine stunting status based on anthropometric variables such as age, weight, and height. The algorithm categorizes data using proximity between samples. Data from the Salatiga City Health Department in 2024 were normalized and encoded for analysis. K-NN was chosen for its ability to provide high-accuracy nutritional classification. Results show that the algorithm achieved 100% accuracy, precision, and recall at certain K values, particularly in the small K range (2-8), demonstrating its effectiveness in identifying nutritional status in toddlers. This study is expected to serve as a reference for utilizing technology and data analysis in early stunting detection, aiding healthcare professionals in designing more targeted and effective interventions. Additionally, it opens opportunities for further development to enhance diagnostic accuracy using machine learning technology.
Pendekatan Machine Learning untuk Deteksi Stunting pada Balita Menggunakan K-Nearest Neighbors Djoru, Ade Putra Tupu; Yulianto, Sri
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 9 No 2 (2025): APRIL-JUNE 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v9i2.3436

Abstract

Stunting is a chronic nutritional problem affecting the physical growth and cognitive development of toddlers, especially in early childhood. This study employs the K-Nearest Neighbors (K-NN) algorithm to determine stunting status based on anthropometric variables such as age, weight, and height. The algorithm categorizes data using proximity between samples. Data from the Salatiga City Health Department in 2024 were normalized and encoded for analysis. K-NN was chosen for its ability to provide high-accuracy nutritional classification. Results show that the algorithm achieved 100% accuracy, precision, and recall at certain K values, particularly in the small K range (2-8), demonstrating its effectiveness in identifying nutritional status in toddlers. This study is expected to serve as a reference for utilizing technology and data analysis in early stunting detection, aiding healthcare professionals in designing more targeted and effective interventions. Additionally, it opens opportunities for further development to enhance diagnostic accuracy using machine learning technology.
Co-Authors Sri Yulianto