Penerapan Metode Resampling dan K-Nearest Neighbor dalam Memprediksi Keberhasilan Studi Mahasiswa Program Magister IPB
Keywords:IPB Master, Imbalanced Data, KNN, Resampling
Graduate School IPB (SPs - IPB) has been established for a long time and is believed to produce high quality graduates and highly competitive. However, based on existing data recaps, there are a small number of students who did not graduate, either resigned or Drop Out (DO). It needs to be handled by conducting a selection process for prospective students based on the profile and educational background S1. One of them by applying the method of classification K - Nearest Neighbor (KNN). The response variable used is the success status of the study of prospective students, ie graduated and not graduated. While the explanatory variables used are the profiles and educational background of prospective students. There is an imbalance of data in the data obtained, where the class does not pass much less than the passing class. This can reduce the value of classification accuracy in minority class (sensitivity). So that the handling of data imbalance by using resampling method, either in the form of Random Over Sampling (ROS), Random Under Sampling (RUS), and Random Over-Under Sampling (ROUS). The result of comparison of evaluation result of KNN classification by using k = 1 to 6, resulted in greater sensitivity value when accompanied by the process of handling the data imbalance than without the process of handling the data imbalance, although the accuracy and specificity value becomes smaller. The greatest sensitivity value was obtained when applying the KNN classification method with k = 1, accompanied by the handling of data imbalance by the RUS method, with the mean and median sensitivity values of 0.89 and 0.90, respectively.