Implementasi Decision Tree Untuk Prediksi Kelulusan Mahasiswa Tepat Waktu

Christin Nandari Dengen, Kusrini Kusrini, Emha Taufiq Luthfi

Abstract


Students who are accepted every year are increasing, but not all students can graduate on time. In achieving graduation, of course, there are stages or processes that must be passed by each student such as following a number of courses, conducting fieldwork practices, real work lectures and final assignment seminars. These processes are carried out within a period of time determined by the University. For this reason, a prediction system for student graduation is needed in order to minimize students who graduate not on time. In predicting student graduation on time using 50 sample data for the 2013 graduation year with gender, IPK, graduation and toefl attributes. This study carried out the application of the CRISP-DM method with the C4.5 algorithm in predicting student graduation. The use of the C4.5 algorithm is supported by simulations carried out using the WeKa application and gets an accuracy value of 60%. With the existence of this research, it is expected to be able to help the Informatics Engineering Program at Universita Mulawarman so that students can graduate on time.


Keywords


Graduation Prediction; CRISP-DM Method; C4.5 Algorithm;

Full Text:

PDF (Indonesian)

References


Ridwan, M. (2017). Sistem Rekomendasi Proses Kelulusan Mahasiswa Berbasis Algoritma Klasifikasi C4.5. Jurnal Ilmiah Informatika, 2(1), 105–111.

Putri, R., & Waspada, I. (2018). Penerapan Algoritma C4 . 5 pada Aplikasi Prediksi Kelulusan Mahasiswa Prodi Informatika. Khazanah Informatika, 4(1), 1–7. Retrieved from http://journals.ums.ac.id/index.php/khif/issue/view/718

Fadillah, A. P. (2018). Penerapan Metode CRISP-DM untuk Prediksi Kelulusan Studi Mahasiswa Menempuh Mata Kuliah (Studi Kasus Universitas XYZ). Jurnal Teknik Informatika Dan Sistem Informasi, 1(3), 260–270. https://doi.org/10.28932/jutisi.v1i3.406

Kusrini, & Luthfi, E. T. (2009). Algoritma Data Mining. (Theresia Ari Prabawati, Ed.). ANDI OFFSET.

Rahma, N. Z., & Setyono, A. (2018). Penerapan Algoritma C4.5 Dalam Memprediksi Kesiapan Siswa SMP IT PAPB Semarang Menghadapi Ujian Nasional. Sisfotenika, 1.

Anastasia, H. G. (2014). Penerapan Data Mining Untuk Mengetahui Faktor-Faktor Yang Mempengaruhi Kelahiran Bayi Menggunakan Association Rules. Jurnal Sarjana Teknik Informatika, 2.

Davies, P. B. (2004). Database Systems Third Edition. New York: Palgave Macmillan.

Fayyad, U. (1996). Advance in Knowledge Discovery and Data Mining. MIT Press.

Witno, S. (2017). Perancangan Aplikasi Prediksi Kelulusan Mahasiswa Tepat Waktu Pada Universitas Buddhi Dharma Menggunakan Perbandingan Algoritma C4.5 dan K-NN. Tech-E, 1(Vol 1 No 1 (2017): Tech-E), 29–36.

Raharjo, R. A. (2017). KAJIAN KOMPARASI PENERAPAN ALGORITMA C4.5 NEURAL NETWORK DAN SVM DENGAN TEKNIK PSO UNTUK PEMILIHAN KARYAWAN TELADAN PT.XYZ. Jurnal String, 1(3), 345–356.




DOI: http://dx.doi.org/10.30700/jst.v10i1.484

Article Metrics

Abstract view : 137 times
PDF (Indonesian) - 134 times

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Badan Pengelola Jurnal Ilmiah Sistem Informasi dan Teknik Informatika (SISFOTENIKA) STMIK Pontianak.

 

Jurnal Ilmiah SISFOTENIKA terindex di :


   

   

  

    

    

    

   

 

 

 

ISSN Printed : 2087-7897

ISSN Online : 2460-5344


SERTIFIKAT PENGHARGAAN :

Jurnal Ilmiah SISFOTENIKA Terakreditasi Peringkat Empat

 

Partners & Co-Organizers:




Lisensi Creative Commons

Jurnal Ilmiah SISFOTENIKA: STMIK Pontianak Online Journal ISSN Printed (2087-7897) - ISSN Online (2460-5344) licensed under a Lisensi Creative Commons Atribusi 4.0 Internasional. Flag Counter

View My Stats>