Analysis of algorithm support vector machine learning and k-nearest neighbor in data accuracy

Abstract

K-Nearest Neighbor is a method of lazy learning method which is a group of instances-based learning. K-NN searches by searching for groups of objects in the training data that are closest to the object on new data or testing data. Support Vector Machine is a learning machine method that works with the aim of finding the best hyperplane that separates two classes in input space. School Achievement is an achievement obtained by serious learning and discipline. The category of outstanding students is to get a good average score and not have an attendance list, especially Absent (A) and a list of late attendance at school can be classified to obtain information on the accuracy of the data being tested. In the testing process both methods obtained good accuracy results between the two methods, namely K-NN obtained an accuracy of 88.52% while SVM is 91.07%.