Implementation of data mining predictions for student her-registration - Bosker Sinaga; Meman Marpaung; Fretty Wandani Ginting; Puspita Sari (SEAN Institute, 2024)
Predictions of an event are carried out to see what will happen in the future, including predictions for students who will register. This is done so that the head of the study program or student affairs will immediately move to approach students who are predicted not to be likely to register. This research implements data mining predictions of student registration. There are often wrong predictions about who might not register. So that the student's approach to decision-making before the time of registration is not carried out by the study program or student affairs. Research Method, namely the survey research method, is a research method carried out using surveys or direct data collection from interested parties, namely the head of the study program. The algorithm used to analyze the data is the K-nearest neighbor algorithm. This research aims to apply data mining to predict student her-registration and to carry out analysis using data mining to predict student her-registration. The research results of applying data mining with the K-Nearest Neighbors algorithm can solve the problem of student enrollment prediction analysis. Of the 10 students who predicted their registration, 7 students would register and 3 students would not register.