Rancang Bangun Aplikasi Data Mining untuk Memprediksi Hasil Belajar Siswa Sekolah Menengah Atas Berbasis Web dengan Algoritma K-NN (Studi Kasus: SMKN 2 Pekanbaru) - Okta Riveranda; Muhammad Ihsan Zul; Maksum Rois Adin Saf (Politeknik Caltex Riau, 2016)
Curriculum 2013 (K-13) was first announced in 2014 which has been applied to number of schools. Preparation of this new curriculum by the government aimed at making education in Indonesia is not only focused on cognitive aspects or skills possessed, but also at students' interest and motivation. Unfortunately, behind the goal, there are issues occured in the school during the application of K-13. Those are input process and values conversion that takes relatively much time. The things are caused by the dissimilarity of the standards and the assessment scale between current curriculum with the previous one. Meanwhile, the academic system running in schools is still pretty conventional. Therefore, this research will construct an application which have capability to handle the things. Beside those additional features, this research is build an application in order to apply the data mining with k-NN algorithm to predict students learning outcomes based on certain subjects. Data source that used in this research were consisted into 500 data training that covered up all classes or labels. Testing methods which have been applied are black box testing and confusion matrix. There are 3 techniques of black box testing that applied in order to test the system functionality according to its input values. Those are equivalence class partitioning, boundary value analysis and decision table based testing. Meanwhile in confusion matrix, it has been done 3 times testing according by k value in k-NN algorithm. With k-5 acquired accurate rate 79.34%, k-10 with accurate rate 62.67%, then k-15 with accurate rate 64%. Thus, information that can conluded from those testing methods is the algorithm with k-5 is more accurate than any others.