PENERAPAN METODE CLUSTERING DENGAN ALGORITMA K-MEANS PADA PENGELOMPOKAN DATA PENGHASILAN ORANG TUA SISWA - Missa, Dionisius; Achmadi, Sentot; Mahmudi, Ali (Institut Teknologi Nasional Malang, 2021)
In Indonesia there are still many students who cannot continue their education because of the high cost of education. One of the reasons for students not continuing their education is the social and economic factors that affect their parents. Therefore, the school does not want students to drop out of school because the parents are unable to pay the full tuition fee each semester of the increase to the right students and are not wrongly targeted so that students can complete their education without being burdened. unpaid tuition fees. In determining usually there are problems because of the large number of student data which causes problems in grouping student data, it is likely that errors will occur when determining students who really have to delay payment. For this research, a system was built in order to be able to apply the K-Means method, in assisting schools in determining the classification of student data that requires a delay in payment every time the semester increases with the right target. So that this process can help schools in shortening the process of processing a lot of student data. While the criteria used in the system are 5, namely father's job, father's income, mother's job, mother's income and number of children. The system is designed using CodeIgniter, PHP, JavaScript and MYSQL as the database. In the process of application testing results that have used the K-Means method for the process of grouping student data that requires late payment, it can produce an accuracy rate of 85%. For this testing process, it is carried out by comparing old student data with the results of the K-Means calculation, so that this system is expected to help the school in classifying student data accurately.