Prediksi Kelulusan Mahasiswa Menggunakan Data Mining Algoritma K-Means - Ray Mondow Sagala (Fakultas Teknologi Informasi - Universitas Advent Indonesia, 2021)
Graduation of a course is very important, if there are students who do not pass a course, especially subjects that have an attachment to another course, must take back the course. Graduation in a course cannot be known before the final exam and final grade calculation are calculated. For this reason, it is necessary to predict the graduation of courses to help anticipate what makes students fail in a course.Through the literature study stage, interviews and looking at academic performance data obtained, the assignment values, unit test values, mid test scores, and attendance obtained from performance data and internal and external factors of students such as class activity, status of residence, language lesson, and the form of the final project given. Data processing is performed using K-means, then calculate the chi square atribute selection and calculate the accuracy of the prediction using a confusion matrix.The results of the study showed that the prediction results using K = 3 of 118 data processed there were 13 students who did not pass, 36 students graduated with sufficient grades, and 69 students graduated with good grades. And what influences the graduation prediction using the chi square atribute is a mid value with ranked atributes of 49, an assignment value of 46, and attendance of 42. Language material using English influences students who graduate with sufficient grades. While the final project in the form of a project or a theory test does not greatly affect the predictions of student graduation in a course. The use of confusion matrix to the prediction results shows an accuracy rate of 93% with precision and recall of 96% and 92%.