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All Journal Jurnal Simetris JAIS (Journal of Applied Intelligent System) Jurnal Dinamika Ekonomi dan Bisnis Jurnal DISPROTEK Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) Jurnal SITECH : Sistem Informasi dan Teknologi Jurnal Kesehatan Primer Walisongo Journal of Information Technology Journal of Dinda : Data Science, Information Technology, and Data Analytics International Journal Software Engineering and Computer Science (IJSECS) Jurnal Minfo Polgan (JMP) Jurnal Talenta Sipil Jurnal Publikasi Teknik Informatika (JUPTI) Jurnal Kesehatan Primer
Teguh Tamrin
UNIVERSITAS ISLAM NAHDLATUL ULAMA JEPARA
Civil Engineering, Building, Construction & Architecture Computer Science & IT Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Nursing Public Health Social Sciences Other

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Stunting Prediction in Toddlers Using the K-Nearest Neighbor (KNN) Method Based on a Web Application at Batealit Community Health Center, Jepara Lisa Falichatul Ibriza; Gentur Wahyu Nyipto Wibowo; Teguh Tamrin
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 3 (2025): DECEMBER 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i3.5553

Abstract

Stunting is still a nutritional problem that exists in Indonesia and it needs immediate intervention in Jepara Regency. At the primary healthcare level, Batealit Public Health Center uses manual anthropometric recording for toddlers' growth assessment. This method can be prone to human recording errors and operational delays which hinder prompt clinical decision-making. To improve this condition, this study develops a web-based system for predicting stunting based on the K-Nearest Neighbor (KNN) algorithm. The research method was applied research with system development using the Waterfall model by processing main variables such as age, weight, and height. We tested the algorithm intensively by trying different neighbor values (k) to obtain the maximum value for accuracy, precision, and recall. From experiments, the KNN algorithm is best at k=3 with a 95.23% accuracy rate; this configuration is better compared to larger k values since they increase misclassification rates on normal and stunted categories. By porting this logic into a web interface, detection moves from being a manual task to an automated one occurring in real-time thus application becomes an essential part of decision support enabling health workers to bypass administrative delays and find stunting much faster more accurately within Batealit service area.
Co-Authors Ahmat Wakit Akhmad Khanif Zyen Akhmad Khanif Zyen Akhmad Khanif Zyen, Akhmad Khanif Anwar, Muhammad Arqom Aprilia Riyana Putri Ariyanto Ariyanto Awwaluddin, Ahmad Habib Aziza, Duwi Nur Bijanto Bijanto Cahyanti, Luluk Dina, Maula Hashina Fitriana, Vera Gun Sudiryanto Harsono, Vilanda Haydar, Muhammad Rifqi Fajrul Heru Saputro Kansania, Istianti Leksono, Bagus Bowo Lisa Falichatul Ibriza Lusi Meriyanti Maula Hashina Dina Mulyo, Harminto Muzakki, Muhammad Alie Nugroho, Muhammad Bayu Nur Aeni Widiastuti R Hadapiningradja Kusumodestoni Ricardus Anggi Pramunendar Sudiryanto, Gun Syamsul Ma’arif Wibowo, Gentur Wahyu Nyipto Yuliana, Alvi Ratna Zaenal Arifin Zaenal Arifin Zainal Abidin Zainal Abidin