Pengelompokan Masyarakat Kurang Mampu Dengan Menggunakan Algoritma K-Means Data Mining - Siagian, Evan Edward; Lubis, Irfansyah Nuddin; Setya, Monita; Sijabat, Ade Dermawan; Sembiring, David JM; Ginting, Meiliyani Br (Forum Kerjasama Pendidikan Tinggi (FKPT), 2025)
Some villages often experience difficulties in classifying economically disadvantaged communities, resulting in the distribution of social assistance sometimes being misdirected. Various grants are received, such as subsidies provided to the poor. Problems encountered include poorly managed community data, which complicates the analysis process, and the lack of a measurable grouping method, which often misdirects aid. Without an objective, data-driven grouping system, aid distribution errors will continue to recur, resulting in misdirected aid. To address these issues, one solution is the use of data mining techniques. In the past, big data management was often done manually or using conventional methods that required significant time, effort, and expense. Data mining is the process of exploring and analyzing large data sets to discover patterns, relationships, or important information that can support decision-making. The K-Means algorithm is a clustering method in data mining used to group data into groups (clusters) based on similar characteristics. The purpose of this study is to design and implement a system for grouping poor communities based on the K-Means algorithm that can assist village governments in distributing aid precisely to targets, accelerate the data analysis process, and reduce aid distribution errors. This study uses 30 population data with 5 attributes: occupation, income, dependents, home ownership, and assets. The method used in this study is the K-Means Algorithm. From the calculations that have been carried out, it is recommended that there are 3 clusters with the same results, namely cluster 1 with 10 residents, cluster 2 with 10 residents, and cluster 3 with 10 residents as well.