Analisis Perbandingan Algoritma Decision Tree (C4.5) Dan K-Naive Bayes Untuk Mengklasifikasi Penerimaan Mahasiswa Baru Tingkat Universitas

Suyadi Suyadi, Arief Setyanto, Hanif Al Fattah


Profile of PMB (New Student Admissions) students from several periods have abundant data that can be used for research. The data is in the form of student information from the majors of origin, NEM and majors now. Classifying the PMB profile data of students at the University level in Yogyakarta can know the majority of learners. Comparing some algorithms is needed to find out the best algorithm. Classification is a grouping algorithm that has several algorithms such as Decision Tree (C4.5) and K-Naive Bayes. Decision Tree (C4.5) is an algorithm with decision tree, while K-Naive Bayes is the likely algorithm that will occur. This analysis uses Rapidminer which is a data analysis software with features of several algorithms that are easy to operate. Both algorithms have results with large data of 1504 students, Decision tree (C4.5) has an accuracy of 81.84% and an error accuracy of 18.16%, while K-Naive Bayes 85.12% and accuracy of error 14.88%. Whereas with smaller data the Decision tree (C4.5) has 100% accuracy whereas K-Naive Bayes has the same accuracy as Decision Tree (C4.5) that is 100%.


Data mining, Classification, Comparison, Decision Tree (C4.5), K-Naive Bayes

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