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The objective of this study is to cluster and classify data using a combination of the k-means and C4.5 methods. The process involves clustering and subsequent classification. The classification process uses k-folds = 10 and samples = stratified sampling. In this study, analphabets in Indonesia of a minimum age of 15 years (15+) were evaluated. The data are the percentage of analogs between 2017 and 2019. The dataset was obtained from and is accessible at In this study, the Davies Bouldin index (DBI) was used to determine the number of clusters with an optimal DBI value of k = 2, namely, 0,121. The results of the cluster maps in Indonesian territories demonstrate low clustering (C 0 = 22 provinces) and high clustering (C 1 = 11 provinces) for children with k = 2 analphabets. Then, the clustering results were classified, and an accuracy of 97.50 was realized, along with a recall of 90.91%, a precision of 100.00%, and an AUC (optimistic) of 0.95 (excellent classification).



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