Implementation of Machine Learning in Determining Nutritional Status using the Complete Linkage Agglomerative Hierarchical Clustering Method - Mutammimul Ula; Ananda Faridhatul Ulva; Mauliza Mauliza; Ilham Sahputra; Ridwan Ridwan (Institute of Computer Science (IOCS), 2021)
Problems that often occur in the nutritional status of children can be done prevention in the form of input to the people of north Aceh on the importance of fulfilling nutrition in toddlers in order to avoid stunting. Lack of nutrition is one of the causes of problems experienced by toddlers in north Aceh. The role of local governments, hospitals and health services is needed in looking at the amount of nutritional status of children, especially areas in northern Aceh. This research aims to be able to determine the nutritional status of toddlers and can provide convenience for hospital officials and doctors in handling gradually and how to treat on a scale in diagnosing diseases with child nutritional status. The first method of this study is to group toddlers identified nutritional status of children who are classified as stunting or not and then grouped areas that are malnourished children using hierarchical agglomerative models. The results of this study can diagnose nutritional status in children with Machine Learning using complete linkage agglomerative hierarchical clustering whose final results can see areas prone to stunting. The data to be modeled consists of 12 sub-districts with samples taken in the form of the number of cases of baktiya 12, dewantara 21, kuta makmur 83, meurah mulia 84, jambo aye 87, nibong 83, sacred store 68. the process of complete linkage agglomerative hierarchical clustering Baktiya method from Scaling Data (standardization)-1.344354111, Kuta Makmur1.376783706, Meurah Mulia 1.415109591, Cot Girek -0.462858762, Simpang Kramat0.801895435, Nisam Antara0.648591896. Based on the results of distance calculations, Prosedure was carried out up to 11 times resulting in cluster groups of 3,21,7,14.15 with a result of 0, clusters 17,23,8,13,18,20,11 with results of 1.6628305 and 1.4,10,19,26,2,9,5,12 with a value of 2.720995. The final calculation of 19,26,1,4,10 is 2.11633.