Fhatiah Adiba, Siswanto Siswanto
Clustering is a data analysis method widely used to obtain information from a set of data. One of the obstacles in using the clustering method is determining the number of clusters and various other influential variables. The clustering method used in this research is clustering self-organizing maps (SOM) using skin disease data. This study aims to determine the optimum variables to define the clustering process variables by evaluating clustering results. This study evaluates the clustering results using internal standards using the silhouette coefficient method. Silhouette coefficient is an evaluation method that combines cohesion and separation methods-clustering with SOM in this study by testing several values to obtain optimal variable values. The variables tested are the number of clusters, epochs, and learning rate. The results of the clustering evaluation using the silhouette coefficient method are 0.316 for each cluster variable, the number of groups K = 4, epoch = 50, and learning rate = 0.5. © 2024 American Institute of Physics Inc.. All rights reserved.
Informatics and Computer Engineering Department, Universitas Negeri Makassar, Makassar, Indonesia; Department of Statistics, Hasanuddin University, Makassar, Indonesia
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