Penerapan K-Optimal Pada Algoritma KNN Untuk Prediksi Kelulusan Tepat Waktu Mahasiswa Program Studi Teknik Informatika - Gadi Ana Amas, Atri; Kopong Pati, Gergorius; Ema Ose Sanga, Felysitas (JOURNAL OF ELECTRICAL AND SYSTEM CONTROL ENGINEERING, 2024)
Decision Making Systems (DSS) are the single most widely used computerized method today. Student satisfaction is determined by the quality that students want, so every university must ensure that this quality is available. The STIMIKOM Stella Maris Sumba Informatics Engineering Program can provide new information that was not previously known using data mining techniques that can be used to predict students' timely graduation. The research uses the k-Nearest Nieghbor method, which is a method for classifying objects based on training data that is closest to the object. Choosing the k value in the kNN algorithm is important because it will affect the performance of the kNN algorithm, therefore it is necessary to know what the k value is and the level of accuracy. The k-Fold Cross Validation method and Accuracy Test are used to determine the k-Optimal value. The result obtained is the value k=5 with an accuracy level of 80.00% which is designated as k-Optimal. The value k=5 is applied to the kNN algorithm to predict students' on-time graduation based on GPA up to semester 4.