Clustering Status Gizi Balita menggunakan Metode K-Means pada Posyandu Desa Mekar Wangi

Muhamad Djaelani, Martanto Martanto, Umi Hayati


The health of children under five is very important in the development of a country. Toddler nutrition is a key aspect in ensuring the healthy growth and development of children. This study aims to analyze the clustering of nutritional status of toddlers in Mekar Wangi village using the K-Means algorithm. Clustering analysis is a data mining analysis method that is influenced by the clustering algorithm method. The nutritional status of toddlers at the posyandu in Mekar Wangi Village is grouped based on certain metrics, such as body weight and height, using the K-Means Clustering technique. Data contains a lot of attribute information. Once the data is collected and analyzed, pre-processing is performed to remove invalid and empty data. The results of the clustering analysis show that some groups of toddlers have normal nutritional status, while other groups have less or more nutritional problems. The optimal Davies Bouldin Index (DBI) performance evaluation value was found using the RapidMiner tool with K2 and the value of 0.164 which is close to 0 indicates that the evaluated cluster produced a good cluster. With a better understanding of the nutritional patterns of toddlers in Mekar Wangi Village, Posyandu officers can developing a more efficient program to improve the nutritional quality of children in Mekar Wangi Village. Posyandu officers can assist in decision making to develop more targeted recommendations and interventions to improve the nutritional status of toddlers in Mekar Wangi village.


K-Means clustering, baby nutrition, posyandu

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