Prediksi Masa Studi Mahasiswa Unama Jambi Menggunakan Metode Algoritma C4.5
DOI:
https://doi.org/10.33998/jurnalmsi.2023.8.1.770Kata Kunci:
Data Mining, Classification, Prediction, C4.5 AlgorithAbstrak
Every year the number of students at The University of Dynamics of the Nation jambi is always increasing
but the students who graduate are different from the number of students who enter. Therefore, the author
conducts a data mining analysis on student data so that it can be used by academic supervisors to find out
the graduation status of students and as a warning so that students can graduate on time so as to reduce the
number of delays in graduation. The author uses student data in 2016 and 2017 as training data and 2020
as testing data as many as 120 training data and 109 testing data and has carried out the data cleaning and
attribute selection process using the forward selection method. In conducting the analysis, the author used
the Tools Weka tool. The author uses the C4.5 algorithm method with 12 attributes, but there are 4 attributes
that are most influential after selecting data using the forward selection method on WEKA. In this case, the
author uses 4 test options, namely 5 Fold Cross Validation, 10 Fold Cross Validation, 70% Percentage Split,
and 80% Percentage Split. The C4.5 Algorithm method produces the largest accuracy value in the training
data, namely 5 Fold Cross Validation by 92.5% and in the testing data by 100%.
Unduhan
Referensi
Jambi, T. dan Jambi, K. B. “Buku Pedoman Akademik Universitas Dinamika Bangsa.”
Jasmir et al. (2018) “Prediksi Mahasiswa Drop Out dengan menggunakan Algoritma Klasifikasi Data
Mining,” Prosiding Annual Research Seminar, 4(1), hal. 82–87. Tersedia pada:
http://seminar.ilkom.unsri.ac.id/index.php/ars/article/view/1864.
Kantardzic, Mehmed (2020), Data Mining: Concepts, Models, Methods, And Algorithms, Edisi ke-3,
New Jersey: John Wiley & Sons, Inc
Widaningsih, S. (2019) “Perbandingan Metode Data Mining Untuk Prediksi Nilai Dan Waktu
Kelulusan Mahasiswa Prodi Teknik Informatika Dengan Algoritma C4,5, Naïve Bayes, Knn Dan
Svm,” Jurnal Tekno Insentif, 13(1), hal. 16–25. doi: 10.36787/jti.v13i1.78.
Mardi, Y. (2017) “Data Mining : Klasifikasi Menggunakan Algoritma C4.5,” Edik Informatika, 2(2),
hal. 213–219. doi: 10.22202/ei.2016.v2i2.1465.
Saefudin, S. dan Fernando, D. (2020) “Penerapan Data Mining Rekomendasi Buku Menggunakan
Algoritma Apriori,” JSiI (Jurnal Sistem Informasi), 7(1), hal. 50. doi: 10.30656/jsii.v7i1.1899.
Amelia, M. winny, Lumenta, A. S. . dan Jacobus, A. (2017) “Prediksi Masa Studi Mahasiswa dengan
Menggunakan Algoritma Naïve Bayes,” Jurnal Teknik Informatika, 11(1). doi:
35793/jti.11.1.2017.17652.
Aji Prasetya Wibawa, Muhammad Guntur Aji Purnama, Muhammad Fathony Akbar, F. A. D. (2018)
“Metode-metode Klasifikasi,” Prosiding Seminar Ilmu Komputer dan Teknologi Informasi, 3(1), hal.
Ramadhan, G. et al. (2020) “Penerapan Data Mining Menggunakan Algoritma C4.5 Dalam Mengukur
Tingkat Kepuasan Pasien BPJS,” Prosiding Seminar Nasional Riset Dan Information Science
(SENARIS), 2, hal. 376–385.
Azis, N. (2021) Perbandingan dan Prediksi Kelulusan Mahasiswa Dengan Weka, Angewandte
Chemie International Edition, 6(11), 951–952.
Haryati, S., Sudarsono, A. dan Suryana, E. (2015) “Implementasi Data Mining Untuk Memprediksi
Masa Studi Mahasiswa Menggunakan Algoritma C4.5 (Studi Kasus: Universitas Dehasen
Bengkulu),” Jurnal Media Infotama, 11(2), hal. 130–138.
Prayoga, N. D. (2018) “Penerapan Algoritma C.45 Dalam Memprediksi Kelulusan Tepat Waktu Pada
Perguruan Tinggi (Studi Kasus : Stmik Royal Kisaran).”
Rohman, A. dan Rufiyanto, A. (2019) “Implementasi Data Mining Dengan Algoritma Decision Tree
C4 . 5 Untuk Prediksi Kelulusan Mahasiswa Di Universitas Pandaran,” Proceeding SINTAK 2019,
hal. 134–139
