Normalisasi Data Dengan Model Min Max Untuk Klasifikasi Calon Mahasiswa Yang Layak Memperoleh KIP Kuliah Dengan Algoritma K-Nearest Neighbor

Abstract

KIP Lectures is a government program that facilitates tuition fees and frees college entrance fees. In the State of Indonesia itself the biggest factor causing the failure of children's education is the problem of poverty, inability to pay. This is what prompted the government to carry out the college KIP program so that every child of the nation can pursue education up to the tertiary level. including at the university of Budi Darma Medan. Receiving college KIP assistance must be right on target, the quota is very limited and the number of students registering, so in determining this to be more effective and efficient it should be done by applying a method that can facilitate the process in the decision to accept KIP college assistance, the method that used to overcome this problem is the K-Nearest Neighbor (KNN). The results obtained in order to facilitate the process of decision making and completion of a number of existing data, the Min-Max normalization method is also used. based on the results of the process that has been carried out with the Rapidminer application with the K-Nearest Neighbor algorithm with a test result of 50% with 2 test data.