Optimization of Naive Bayes Using Genetic Algorithm as Fiture Selection for Predicting Student Performance

Abstract

In this globalisation era, the morality tenegers decrease.This fenomena can be seen on mass or electronic media. Mass or electronic media inform that the negatif case often happend on teenegers community. Negatif case such as brawl, drug, gambling, rape, disobidience to parents, and others. The cause of negatif case is not from himself or hisself but it is triggered by bad customs. The less of parent attention, the low of parent relation quality can inflict bad customs from children. Parent education, parent job, the parent support of education can influence children mainset. How long time children study, how long time children have sparetime, how long time children make friend, and how long time children acess internet can influence mainset of children. The customs of children explained on sentences before, can be measured by science and tecnology. Data Mining that is branch of computer science can measure how much quality children or adult perform based on custom framer indicator. In the last research of student performance using Naive Bayes Methode, the number of attribute is too much (33 attribut) and the score of accuracy is 91.15 %. In this research, the researcher optimize attributes of the last research using Genetic Algorithm. Genetic Algorithm can choose relevant attribut. The choice of relevant attributes can increase score of accuracy. The score of accuracy after using Genetic Algorithm is 97.21 %.