Stunting Early Warning Application Using KNN Machine Learning Method

Nani Purwati; Gunawan Budi Sulistyo

doi:10.34288/jri.v5i3.228

Authors

Nani Purwati Universitas Bina Sarana Informatika
Gunawan Budi Sulistyo Universitas Bina Sarana Informatika

(*) Corresponding Author

DOI:

https://doi.org/10.34288/jri.v5i3.228

Keywords:

Application, Classification, k-NN, Stunting

Abstract

Stunting in toddlers is defined as a condition of failure to thrive due to chronic malnutrition in the long term. The problem of stunting in Indonesia is an issue that is still a concern for the Indonesian government. The prevalence of stunting in Indonesia is still relatively high, coupled with the COVID-19 pandemic, which has impacted the economic sector. For this reason, research on stunting is still a critical topic. This study aims to classify toddler stunting using the k-Nearest Neighbor classification algorithm and build a website-based early detection application for toddler stunting cases. The research results using the k-Nearest Neighbor Algorithm trial obtained a relatively high accuracy of 92.45%. Implementing an early detection system for stunting cases has proven to help health workers classify toddlers as stunted or not. This application is also helpful as an archive and facilitates data reporting. The application has eight main menus: the Puskesmas data menu, Posyandu data, toddler data, weighing, weighing results, development menu, and stunting early warning menu, which contains malnourished and stunted toddlers.

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