Predicted Student Study Period with C4.5 Data Mining Algorithm
Agus Supriyanto, Dwi Maryono, Febri Liantoni
Abstract
Data of alumni from 2012 to 2015 found that the average percentage of students graduating on time was 22%. The comparison between the number of students who graduate on time and new students who enter each year is not comparable, therefore a study is needed to find out the factors that affect student graduation and to prediction of the graduation period of the student through data mining research using the C4.5 algorithm. The data tested was student alumni data from 2012 to 2015. The instruments studied include study period, academic year, GPA, corner focus, gender, intensity of work during college, type of thesis, intensity of campus internal organization, intensity of external organization of campus, UKT group, scholarship status, pre-college education, hobby intensity, intensity of game play, academic competition participation status, non-academic competition participation status, and availability of facilities and infrastructure. The best test results using percentage-split 75% obtained 83.33% accuracy as well as the rules contained in the decision tree.
Keywords
C4.5 algorithm; Data mining; Predicted study period
References
Agustina, D. melina, & Wijanarto. (2016). Analisis Perbandingan Algoritma ID3 Dan C4 . 5 Untuk Klasifikasi Penerima Hibah Pemasangan Air Minum pada PDAM Kabupaten Kendal. Journal of Applied Intelligent System, 1(3), 234–244.
Astuti, I. P. (2017). Prediksi Ketepatan Waktu Kelulusan Dengan Algoritma Data Mining C4 . 5. 2(2).
Dhika, H. (2015). Kajian Komparasi Penerapan Algoritma C4 . 5 , Naïve Bayes , dan Neural Network dalam Pemilihan Mitra Kerja Penyedia Jasa Transportasi : Studi Kasus CV . Viradi Global Pratama. 197–202.
Kasus, S., Dehasen, U., Haryati, S., Sudarsono, A., & Suryana, E. (2015). IMPLEMENTASI DATA MINING UNTUK MEMPREDIKSI MASA STUDI MAHASISWA MENGGUNAKAN ALGORITMA C4 . 5. 11(2), 130–138.
Mustafa, M. S., Ramadhan, M. R., & Thenata, A. P. (2018). Implementasi Data Mining untuk Evaluasi Kinerja Akademik Mahasiswa Menggunakan Algoritma Naive Bayes Classifier. Creative Information Technology Journal, 4(2), 151. https://doi.org/10.24076/citec.2017v4i2.106
