Grouping of Toddlers with Malnutrition Based on Provinces in Indonesia Using K-Medoids Algorithm

Authors

  • Sri Anita Siallagan STIKOM Tunas Bangsa Pematangsiantar, North Sumatra
  • M. Safii AMIK Tunas Bangsa Pematangsiantar, North Sumatra

Keywords:

Data Mining, K-Medoids, Clustering, Malnutrtion

Abstract

Malnutrition is a poor health condition in infants and toddlers caused by a lack of nutritional intake. Babies and toddlers who suffer from malnutrition will experience conditions of slowness in development, slowness in thinking, underweight and so on. Malnutrition can be prevented by complete immunization from birth, providing good nutrition for their development, and so on. The purpose of this study was to determine the results of the grouping of provinces with the highest malnutrition sufferers using the K-Medoids method which is part of Data Mining. The K-Medoids method is a clustering method that can break the dataset into several groups. In this study, the data used were sourced from the Central Statistics Agency in 2016 – 2018. The results of this clustering will later show the province which is the toddler with the highest malnutrition. This research is expected to provide information for the government regarding the grouping of children under five with malnutrition in Indonesia.

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Published

2021-10-14

How to Cite

Siallagan, S. A. ., & Safii, M. (2021). Grouping of Toddlers with Malnutrition Based on Provinces in Indonesia Using K-Medoids Algorithm. Journal of Artificial Intelligence and Engineering Applications (JAIEA), 1(1), 47–53. Retrieved from https://ioinformatic.org/index.php/JAIEA/article/view/53