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Muhammad Aprilsyah
Universitas Islam Negeri Sumatera Utara, Medan
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Kombinasi K-Nearest Neighbor dengan K-Means Clustering Klasifikasi Stunting pada Bayi Berbasis Website Muhammad Aprilsyah; Raissa Amanda Putri
JURIKOM (Jurnal Riset Komputer) Vol 12, No 2 (2025): April 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i2.8498

Abstract

Stunting is a serious health issue caused by insufficient nutrition over an extended period, especially in young children. This study aims to develop a web-based stunting data classification system using a combination of K-Means Clustering and K-Nearest Neighbors (K-NN) algorithms. The dataset used is sourced from the Health Department of Medan City in 2021-2024, consisting of 683 data entries. The research process includes problem identification, data gathering conducted through observations and interviews, data preprocessing using StandardScaler, and splitting the dataset into 70% training and 30% testing datasets. The K-Means technique is utilized for data segmentation based on z-score values. The clustering results are then used as labels for classification with K-NN. The system implementation shows a classification result with a distribution of 6.9% for mild stunting, 25.8% for moderate stunting, and 67.3% for severe stunting. The results indicate that the combination of K-Means and K-NN produces more accurate classification compared to using a single method. This study is expected to assist the Health Department of Medan City in analyzing stunting data more efficiently and contribute to the future development of stunting classification systems.
Co-Authors Raissa Amanda Putri