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Data Mining is a method that is often needed in large-scale data processing, so data mining has important access to the fields of life including industry, finance, weather, science and technology. In data mining techniques there are methods that can be used, namely classification, clustering, regression, variable selection, and market basket analysis. Illiteracy is one of the factors that hinder the quality of human resources. One of the basic things that must be fulfilled to improve the quality of human resources is the eradication of illiteracy among the community. The purpose of this study is to determine the clustering of illiterate communities based on provinces in Indonesia. The results of the study are illiterate data clustering according to the age proportion of 15-44 namely 1 high group node, low group has 27 nodes, and medium group 6 nodes. The results of this study become input for the government to determine illiteracy eradication policies in Indonesia based on provinces.    Kata Kunci :  Illiterate  , Data mining, K-Means Clustering    Data Mining termasuk metode yang sering dibutuhkan dalam pengolahan data berskala besar, maka data mining mempunyai akses penting pada bidang kehidupan diantaranya yaitu bidang industri, bidang keuangan, cuaca, ilmu dan teknologi. Pada teknik data mining terdapat metode-metode yang dapat digunakan yaitu klasifikasi, clustering, regresi, seleksi variabel, dan market basket analisis. Buta huruf merupakan salah satu faktor yang menghambat kualitas sumber daya manusia. Salah satu hal mendasar yang harus dipenuhi untuk meningkatkan kualitas sumber daya manusia adalah pemberantasan buta huruf di kalangan masyarakatAdapun tujuan penelitian ini adalah menetukan clustering masyarakat buta huruf   berdasarkan propinsi di Indonesia  .Hasil dari penelitian adalah data clustering buta huruf menurut propisi umur 15-44 yaitu   1 node   kelompok tinggi  ,  kelompok rendah memiliki 27 node  , dan kelompok  sedang  6 node. Ha  sil penelitian ini menjadi bahan masukan kepada pemerintah untuk menentukan kebijakanpemberantasan buta huruf di Indonesia berdasarakn propinsi  .    Kata Kunci : Buta Huruf, Data mining,  K-Means Clustering

PENGELOMPOKAN PERSENTASE BUTA HURUF UMUR 15-44 MENURUT PROVINSI MENGGUNAKAN ALGORITMA K-MEANS