Analisis Clustering Menggunakan Metode K-Means untuk Mengelompokkan Kabupaten/Kota di Indonesia berdasarkan UnsurUnsur Pembangun Literasi Masyarakat (UPLM) - Jelita, Mutia (Politeknik Statistika STIS, 2024)
The government needs to identify which districts/cities require more guidance in the library sector by utilizing the data obtained. This study aims to conduct clustering analysis using the K-Means method to categorize districts/cities in Indonesia based on the Elements of Community Literacy Development (UPLM) data in 2023. The Elbow method is applied to determine the optimal number of clusters. The results of the study reveal four clusters: Cluster I consists of 62 districts/cities with characteristics of having four high-value UPLMs; Cluster II includes 84 districts/cities with no high-value UPLMs; Cluster III encompasses 222 districts/cities with one high-value UPLM; and Cluster IV includes 146 districts/cities with two highvalue UPLMs. Based on these clusters, the government, particularly the National Library of Indonesia, can focus on providing more targeted guidance, especially in Cluster II, which includes districts like Bener Meriah, Indragiri Hilir, Bogor, Sikka, and Yahukimo.