Aplikasi Data Mining untuk Memprediksi Mahasiswa Berpotensi Drop Out menggunakan Algoritme K-Nearest Neighbor (K-NN) - Mardiani Putri Agustini; Ahmad Afif Supianto; Welly Purnomo (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2019)
Drop out is a problem related to the success of student learning. This problem has also happened in Information System study program at Brawijaya University. The results of interviews were conducted with the Head of the Information System Study Program that there was a drop out every year. The existence of students who drop out can cause a decrease in the quality of higher education. Therefore, as handling of these problems needs a system that capable to help make decisions to predict on students who have the potential to drop out so prevention can be done. This system is expected to be able to help the Brawijaya Information System Study Program in making decisions, become the material for early evaluation and provide early treatment for students who have the potential to drop out. One technique for predicting is to use data mining. Classification using K-Nearest Neighbor (K-NN) algorithm is one of data mining method that can be used to predict student drop out potential. The results of processing with the help of Weka tool found the best proximity value using the K-NN algorithm is k=5. The results of evaluating algorithms obtained using confusion matrix have an accuracy rate of 99.2337%. The AUC value result of ROC curve shows a value of 0.8918. The level of usability testing generated by utilizing SUS is 67.