Application Development of Student's Graduation Classification Model based on The First 2 Years Performance using K-Nearest Neighbor - Prasetyawan P; Faridz Abadi M (Bandar Lampung University (UBL), 2017)
A College keeps a lot of data such as, academic data, administration, student biodata and others. The existing student data has not been fully utilized. In the student education system is an important asset for an educational institution and for that it is necessary to note the graduation rate of students on time. Differences in the ability of students to complete the study on time required the monitoring and evaluation, so that it can find new information or knowledge to make decisions. The purpose of this study, to know the relationship between IP variables Semester 1, IP Semester 2, IP Semester 3, IP Semester 4, Gender, Student Status on Student Study Duration using k-nearest neighbor algorithm. The result of this research in the classification of students' graduation using the knn algorithm based on student status, gender, ip semester 1 - ip semester 4 with k-fold cross validation in can mean value of K1 accuracy 88%, K3 accuracy 88.67%, K5 accuracy of 93.78%, K7 86% accuracy, K9 accuracy 86.22%, K11 accuracy 92.44%, K13 accuracy 89.55%, K15 accuracy 93.78%, K17 accuracy 99.78%, and K19 accuracy 100 %. Of the 500 training data in the status of 188 students, 312 students, the status of students work longer in completing the lecture and in the gender of 290 men, 210 women, then women longer in finishing college. Finding the optimal k value using k-fold cross validation. The result of accuracy using k-fold cross validation is K19 with 100% accuracy