PENERAPAN ALGORITMA K-MEANS UNTUK CLUSTERING SANTRI PRA-SEJAHTERA DI YAYASAN BANTUAN SOSIAL (YBS) AZ-ZAINIYYAH PONDOK PESANTREN NURUL JADID - Sudriyanto Sudriyanto; Ahmad Khairi; Atoillah Shohibul Hikam (Computer Society of Nahdlatul Ulama (CSNU) Indonesia, 2023)
The problem of poverty is a social problem that has a sizeable impact in various countries, not only in a large scope, but even on a small scale, poverty is a problem that needs special attention, one example is within the scope of Islamic boarding schools. In this study, the dataset was obtained from Pedatren, namely data from SLTP and SLTA Nurul Jadid Islamic Boarding School students in 2021. The algorithm used in this research is the K-Means Clustering method. The K-Means Clustering method is an Unsupervised technique, as well as a method of grouping data into several groups, according to each other's characteristics. The advantages of the K-Means algorithm are relatively simple and easy to implement, scalable for large datasets, easily adaptable to new examples, commonly implemented to clusters of different shapes and sizes. The amount of data used is 749 junior and senior high school students data, divided into 4 clusters namely (C1) Pre-Prosperous Families (C2) Prosperous Families 1 Prosperous Families 2 (C3) and Prosperous Plus Families (C4). As for the phase that done in this research such as identifying a problem, a literature review, data collection, prepocessing, the implementation of k-means algorithm clustering and the last evalusi the results of. From the results of the grouping of clusters of data or C1 = 163, C2 = 215, C3 = 246 and C4 = 125.The end of use evalusi matrix davies bouldin mem-peroleh akurai 0,90 value index.