Analisis Komparasi Algoritma Naïve Bayes Dan C4.5 Dalam Memprediksi Kelulusan Mahasiswa (Studi Kasus: Jurusan S1 Teknik Informatika Universitas Papua)

Comparative Analysis of Naïve Bayes Algorithm and C4.5 in Predicting Student Graduation (Case Study: Department of Informatics Engineering, University of Papua)

Authors

  • Muhammad Fikri Indriawan
  • Julius P. P. Naibaho
  • Marlinda Sanglise

Keywords:

Data Mining, Klasifikasi, C4.5, Naive Bayes, Perbandingan Algoritma, RapidMiner, Prediksi Kelulusan Mahasiswa

Abstract

The number of students who graduate on time is an important indicator that must be considered considering that it is included in the internal quality assurance standards of a university and can be useful for students themselves when entering the world of work. To predict the pass rate, data mining techniques can be used with a classification function where the algorithm examples are Naïve Bayes, kNN (K-Nearest Neighbor), C4.5, and SVM (Support Vector Machine). With so many algorithms that can be used, it is important to know which algorithm is the most effective in classifying data according to the case under study. So that in this study a comparison of classification algorithms in this case is Naïve Bayes and C4.5 and from the analysis process that has been carried out it can be concluded that the C4.5 algorithm is a more effective algorithm used to predict student graduation than Nave Bayes with accuracy values of 76,23%, precision of 50,00%, recall is 19,05%, error is 23,77% and AUC value is 0.669.

Additional Files

Published

2022-11-15