Model Prediksi Kelulusan Mahasiswa Menggunakan Decision Tree C4.5 dan Software Weka

  • Isnan Mulia Institut Bisnis dan Informatika Kesatuan
  • Muanas Muanas Institut Bisnis dan Informatika Kesatuan

Abstract

In this research, we build a model to predict graduation status of students in Institut Bisnis dan Informatika Kesatuan using C4.5 decision tree algorithm. The prediction model is built using students’ GPA from semester 1 to semester 4, for students with admission year of 2013 to 2016. The prediction model obtained is a decision tree with 26 rules, with the attribute IPS_4 being the attribute that determines the graduation label of students. This prediction model yields an accuracy of 73%, a result that is not good enough. This result is probably due to unbalanced proportion of the data used.

Downloads

Download data is not yet available.

References

Amin, Fira Nurahmah Al, Indahwati, and Yenni Angraini. 2013. “Analisis Ketepatan Waktu Lulus Berdasarkan Karakteristik Mahasiswa FEM Dan Faperta Menggunakan Metode Chart.” Xplore: Journal of Statistics 1 (2): 1–8. https://doi.org/10.29244/xplore.v1i2.12411.
Frank, Eibe, Mark Hall, Geoffrey Holmes, Richard Kirkby, Bernhard Pfahringer, Ian H. Witten, and Len Trigg. 2009. “Weka-A Machine Learning Workbench for Data Mining.” In Data Mining and Knowledge Discovery Handbook, 1269–77. Boston, MA: Springer US. https://doi.org/10.1007/978-0-387-09823-4_66.
Han, Jiawei, Micheline Kamber, and Jian Pei. 2012. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers. 3rd ed. Massachusetts: Morgan Kaufmann Publishers.
Kementerian Pendidikan dan Kebudayaan Republik Indonesia. 2020. PERATURAN MENTERI PENDIDIKAN DAN KEBUDAYAAN REPUBLIK INDONESIA NOMOR 3 TAHUN 2020 TENTANG STANDAR NASIONAL PENDIDIKAN TINGGI.
Kurniawan, Donny, Anthony Anggrawan, and Hairani Hairani. 2020. “Graduation Prediction System On Students Using C4.5 Algorithm.” MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer 19 (2): 358–65. https://doi.org/10.30812/matrik.v19i2.685.
Meiriza, Allsela, Endang Lestari, Pacu Putra, Ayu Monaputri, and Dini Ayu Lestari. 2020. “Prediction Graduate Student Use Naive Bayes Classifier.” In Proceedings of the Sriwijaya International Conference on Information Technology and Its Applications (SICONIAN 2019). Paris, France: Atlantis Press. https://doi.org/10.2991/aisr.k.200424.056.
Nafie Ali, Faisal Mohammed, and Abdelmoneim Ali Mohamed Hamed. 2018. “Usage Apriori and Clustering Algorithms in WEKA Tools to Mining Dataset of Traffic Accidents.” Journal of Information and Telecommunication 2 (3): 231–45. https://doi.org/10.1080/24751839.2018.1448205.
Quinlan, J R. 1996. “Improved Use of Continuous Attributes in C4.5.” Journal of Artificial Intelligence Research 4 (March): 77–90. https://doi.org/10.1613/jair.279.
Solichin, Achmad. 2019. “Comparison of Decision Tree, Naïve Bayes and K-Nearest Neighbors for Predicting Thesis Graduation.” In 2019 6th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), 217–22. IEEE. https://doi.org/10.23919/EECSI48112.2019.8977081.
Suhaimi, Nurafifah Mohammad, Shuzlina Abdul-Rahman, Sofianita Mutalib, Nurzeatul Hamimah Abdul Hamid, and Ariff Md Ab Malik. 2019. “Review on Predicting Students’ Graduation Time Using Machine Learning Algorithms.” International Journal of Modern Education and Computer Science 11 (7): 1–13. https://doi.org/10.5815/ijmecs.2019.07.01.
Witten, Ian H., Eibe Frank, and Mark A. Hall. 2011. Data Mining: Practical Machine Learning Tools and Techniques. 3rd ed. Massachusetts: Morgan Kaufmann.
Published
2021-06-10
How to Cite
MULIA, Isnan; MUANAS, Muanas. Model Prediksi Kelulusan Mahasiswa Menggunakan Decision Tree C4.5 dan Software Weka. JAS-PT (Jurnal Analisis Sistem Pendidikan Tinggi Indonesia), [S.l.], v. 5, n. 1, p. 57 - 64, june 2021. ISSN 2620-5718. Available at: <http://journal.fdi.or.id/index.php/jaspt/article/view/417>. Date accessed: 18 oct. 2021. doi: https://doi.org/10.36339/jaspt.v5i1.417.