Application of the K-Nearest Neighbor (KNN) Algorithm for Stunting Diagnosis in Infants Aged 1-12 Months

Moh abdul kholik; Cucut Hariz Pratomo; Sapriani Gustina

doi:10.32493/informatika.v9i2.40983

Authors

Moh abdul kholik Universitas Surakarta
Cucut Hariz Pratomo Universitas Muhammadiyah Karanganyar
Sapriani Gustina Universitas Proklamasi 45

DOI:

https://doi.org/10.32493/informatika.v9i2.40983

Keywords:

Diagnose, Artificial intelligence, Detection, K-Nearest Neighbor (KNN), Stunting, Machine Learning

Abstract

Stunting in toddlers must be addressed immediately because it has a negative impact on their growth and development. Stunting is a disorder where toddlers experience chronic malnutrition, thus their physical growth and height do not match their age. According to the Indonesian Nutritional Status Survey (SSGI), stunting is more common among toddlers from aged 0 to 1 year than overall. Stunting can have short-term and long-term impacts. This research examines data from the Temanggung District Health Service on 3,999 toddlers aged 0 to 12 months between 2019 and 2022. Many studies have exclusively looked at stunting in children aged one to five years, especially research on stunting using the KNN method, even though stunting can actually be recognized from an early age. Therefore, researchers are more specific in using the KNN method for cases of babies 1 to 12 months so as to differentiate it from previous researchers. The aim of this research is to use the K-Nearest Neighbor (KNN) algorithm to detect stunting nutritional status in toddlers. K-Nearest Neighbor (KNN) is a classification algorithm that uses a set of K values from the closest data (its neighbors) as a reference to determine the class of incoming data. KNN classifies data based on its similarity or closeness to other data. The dataset used includes parameters of age, gender and height. The research approach is the CRISP-DM (Cross Industry Standard Process for Data Mining) method, which begins with business knowledge, followed by EDA and modeling, evaluation, testing and report preparation. The result shows that the KNN algorithm can accurately categorize children as stunted or not based on age (U) and height (TB), with the maximum level of accuracy and the lowest error rate at k = 5. At this optimal value (k), this algorithm has an accuracy of 99.87%, Recall 99.84%, and precision 99.73.

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