Prediction of Student Graduation using the K-Nearest Neighbors Method - Wati, Embun Fajar; Perangin-Angin, Elvi Sunita; Sari, Anggi Puspita (Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa, 2023)
Predictions on the accuracy of student graduation are designed to support study programs in guiding students so that they can graduate on time. The number of student graduations will influence the university's accreditation score. Graduation predictions can provide very useful information in decision-making; therefore, research was conducted on student graduation data. This data will be processed using the K-Nearest Neighbor method. The dataset used consisted of 150 students majoring in informatics engineering. The variables included gender, age, marital status, grade, and job status. The research methodology used in this study consists of 6 stages: Data Collection, Data Selection, Preprocessing, Transformation, Testing, and Evaluation. In the preprocessing or cleaning stage, the data can be fully utilized because all fields have been filled in correctly. Meanwhile, in the transformation stage, the data is categorized as follows: age (young: 19-24, old: 25-50) and grade (large: 3-4, small: 1-2.9). The K-Nearest Neighbor (KNN) method can predict student graduation rates. The KNN method, processed with the RapidMiner 9.9 tool, obtained an average accuracy of 100%. Based on the results of 100% accuracy and an AUC value of 1, it can be concluded that the KNN method is highly accurate in classifying graduation data for the 150 students.