Implementasi Algoritme Fuzzy K-Nearest Neighbor untuk Penentuan Lulus Tepat Waktu (Studi Kasus : Fakultas Ilmu Komputer Universitas Brawijaya) - Andhika Satria Pria Anugerah; Indriati Indriati; Candra Dewi (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2018)
Along with the increasing interest of studying in the collage, therefore the data of student graduation which is filed will keep increasing. However, those data could be in a very large amount if it is processed manually, therefore it is needed to apply the student graduation classification which able to classify the graduation data based on the determined parameters. There are some ways to classify the object that have been developed, one of them is Fuzzy K-Nearest Neighbor. Fuzzy K-Nearest Neighbor is one of the methods which is used to classify the object by calculating the membership degree in each class. The experiment of Fuzzy K-Nearest Neighbor is done toward the problem of time of student graduation which is categorized into graduate on time and graduate out of time. In this experiment, Fuzzy K-Nearest Neighbor is used to identify the students based on the achievement index that they have got. Based on the experiment results, Fuzzy K-Nearest Neighbor is able to get an accuracy score around 98%. This accuracy is from the given weight of the membership in each output class. This is able to minimize the doubtful in determining the output class