Kombinasi K-Nearest Neighbor dengan K-Means Clustering Klasifikasi Stunting pada Bayi Berbasis Website

Muhammad Aprilsyah; Raissa Amanda Putri

doi:10.30865/jurikom.v12i2.8498

Authors

Muhammad Aprilsyah Universitas Islam Negeri Sumatera Utara, Medan
Raissa Amanda Putri Universitas Islam Negeri Sumatera Utara, Medan

DOI:

https://doi.org/10.30865/jurikom.v12i2.8498

Keywords:

Stunting, K-Means Clustering, K-Nearest Neighbors, Classification, Data Mining

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.

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