Open AccessComparison of K-Nearest Neighbour and support vector machine for choosing senior high school
Author(s) -
Mohamad Irfan,
Ade Rahmat Nurhidayat,
Agung Wahana,
Dian Sa’adillah Maylawati,
Muhammad Ali Ramdhani
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1280/2/022026
Subject(s) - support vector machine , computer science , data mining , artificial intelligence , k nearest neighbors algorithm , machine learning , knowledge extraction , pattern recognition (psychology) , value (mathematics)
The aim of this research is to compare K-Nearest Neighbour (KNN) and Support Vector Machine (SVM) algorithm which are used for choosing senior high school recommendation. As we know that education is an important aspect in the development of a nation. The methodology that used in this research is data mining through Knowledge Discovery in Database (KDD) stages, which consist of cleaning, data integration, data selection, data transformation, data mining, pattern evaluation, and knowledge presentation. KNN and SVM are the most common algorithms used in data mining and decision support system. Either KNN or SVM in this research used for classifying the type of senior high school by input parameters, among others national examination score, student interest, and counsellor suggestion. Based on the experiment with several training and testing data, the result shows that SVM is better than KNN. SVM has an accuracy value around 97.1%, while KNN has an accuracy value around 88.5%. And also, SVM has processing time faster than KNN.