Analysis of Application Naive Bayes Algorithm and Decision Tree in Predicting Student Readiness in Facing National Exams
Hermanto (a*), Antonius Yadi Kuntoro (b*), Sumpena Suhandi (c*), Yuma Akbar (d*), Nirat (e*), Nugroho Febianto (f*), Sfenrianto (g*)
a) Master of Computer Science Postgraduate Program, STMIK Nusa Mandiri, Jakarta Indonesia
*hermanto.hmt[at]bsi.ac.id
b) Master of Computer Science Postgraduate Program, STMIK Nusa Mandiri, Jakarta Indonesia
*antonius.aio[at]nusamandiri.ac.id
c) Master of Computer Science Postgraduate Program, STMIK Nusa Mandiri, Jakarta Indonesia
*sumpenasuhandi[at]gmail.com
d) Master of Computer Science Postgraduate Program, STMIK Nusa Mandiri, Jakarta Indonesia
*stikomcki[at]stikomcki.ac.id
e) Master of Computer Science Postgraduate Program, STMIK Nusa Mandiri, Jakarta Indonesia
*mrnirat[at]gmail.com
f) Master of Computer Science Postgraduate Program, STMIK Nusa Mandiri, Jakarta Indonesia
*irawanf81[at]gmail.com
g) Information System Management Department, BINUS Graduate Program - Master of Information Systems Management , Bina Nusantara University, Jakarta, Indonesia 11480
*sfenrianto[at]binus.edu
Abstract
Trial is the evaluation phase facing the National Examination indeed, readiness in the exam is very important to be done by students in grade 3, testing the practice of the final exam for students in grade 3 will take the national examination, namely, schools, teacher guidance, and counseling have important roles in providing services to students. Trial services prior to student national exams require special professional handling, because they involve the success of students in national examinations. Errors in determining the readiness of students for national examinations can be a negative influence on the process and results of the students National Examination itself. After comparison of the Naive Bayes algorithm and the Decision Tree, the prediction results are predicted to predict the readiness of students to face the national exam. It is proven that the naive bayes algorithm has an accuracy value of 82.18% and the AUC value of -0.871 is included in the fair classification, while the Decision Tree algorithm has an accuracy of 73.45% and the AUC value of 0.696 belongs to poor classification. From these results it can be concluded that the naive bayes algorithm has a higher accuracy compared to the Decision Tree algorithm, it can be seen the difference in accuracy between naive bayes while the difference between naive bayes and the Decision Tree is 8.73%. Thus the naive bayes algorithm can predict the readiness of students to face the national exam better.
Keywords: Student, National Examination, Naive Bayes, Decision Tree
Topic: International Symposium of Engineering, Technology, and Health Sciences