Implementasi Metode K-Medoids Clustering Dalam Pengelompokan Data Penyakit Alergi Pada Anak

Abstract

Allergies are an abnormal response from the immune system. People who experience allergies have an immune system that reacts to a substance that is usually harmless in the environment. There are two limitations in this study, namely, seafood allergy and air allergy. In this study, the data used were sourced from the National Statistics Agency in 2011-2019. This study uses data mining techniques in data processing with the k-medoids clustering method. The k-medoids method is a clustering method that functions to split the dataset into several groups. The advantages of this method are able to overcome the weaknesses of the k-means method which is sensitive to outliers. Another advantage of this method is that the results of the clustering process do not depend on the order in which the dataset is entered. This method can be applied to data on the percentage of children affected by allergies by province, so that it can be seen the grouping of provinces based on this data. From this grouping data obtained 3 clusters namely low cluster (2 provinces), medium cluster (30 provinces) and high cluster (2 provinces) from the percentage of allergy immunization under five in each province. It is hoped that this research can provide information to the health department, especially the public health center regarding data grouping of Allergic Diseases in children in Indonesia which has an impact on equity in giving anti-allergic immunization to children in Indonesia