ALGORITMA SUPPORT VECTOR MACHINE UNTUK MEMPREDIKSI NILAI UJIAN NASIONAL - Emi Rizky; Purwanto Purwanto; Heribertus Himawan (Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro, 2020)
In order to improve the quality of graduate education through an exam in order to compete in domestic, regional and international levels and therefore require the achievement of national standards through the National Examination (UN). produce test scores that boast with the title and can pass the National Exam, due to lack of graduates when the National Examination become routine issues annually. This problem is felt by students, parents, teachers, educational units and agencies associated with other national exams. By looking at the reasons we need a prediction to predict the value of the UN. Soft computing has several abilities one of which is a technique that can be used to predict the ability of students to acquire the methods of the National Examination Support Vector Machine (SVM) which is a branch of artificial intelligence where the processing system configuration information obtained performance model for the prediction of the National Examination the Root mean squared Error (RMSE) is the best for Indonesian was 0.713 + / - 0.173, English at 0586 + / - 0.066, and Mathematics by 0882 + / - 0188. configuration with predictions using a barometer. k-fold 10, C (cost) of 0.1 with kernel-type radial Indonesian subjects, k-fold 10, C (cost) of 0.3 with radial kernel type for the subjects of English and Mathematics.