Development of Graduation Prediction Model for Industrial Engineering Students Using Decision Tree

Rifki Muhendra


Data mining is one of the rapidly growing data processing sciences. One application of data mining is to build a model of student graduation criteria. In this study, a prediction model for student graduation criteria was developed using a decision tree (DT). Several factors that influence the graduation criteria of students studied in this study include GPA, field of study, age, number of credits completed, and so on. The development of this model uses the open source Rapidminer software which is proven to have ease in processing but is very good at producing models. There are 3 prediction models produced, namely the DT model using the Gini Index method, Information gain and gain ratio. The resulting model has a fairly large root distribution in the predicate is very satisfactory. This means this predicate in the process does quite a lot of iterations. These three models can be used to predict student graduation because they have an accuracy and Kappa value greater than 80%. This shows that this model has a high level of confidence and can describe what is happening. The Gini Index model has the highest accuracy and kappa value compared to the information gain and gain ratio models with accuracy and kappa values of 0.963 and 0.932, respectively. This shows that the Gini Index model is superior for processing large data.


Data Mining; Prediction Model; Student Graduation Criteria; Decision Tree

Full Text:



Aprilla Dennis (2013) ‘Belajar Data Mining Dengan Rapidminer’, Innovation And Knowledge Management In Business Globalization: Theory & Practice, Vols 1 And 2, 5(4).

Arabshahi, H. And Fazlollahtabar, H. (2018) ‘Classifying Innovative Activities Using Decision Tree And Gini Index’, International Journal Of Innovation And Technology Management, 15(3). Doi: 10.1142/S0219877018500256.

Arsad, P. M., Buniyamin, N. And Manan, J. A. B. (2014) ‘Students’ English Language Proficiency And Its Impact On The Overall Student’s Academic Performance : An Analysis And Prediction Using Neural Network Model’, WSEAS Transacions On Advances In Engineering Education, 11.

Bassi, J. S. Et Al. (2019) ‘Students Graduation On Time Prediction Model Using Artificial Neural Network’, IOSR Journal Of Computer Engineering (IOSR-JCE), 21(3).

Espinoza, O. Et Al. (2019) ‘Factors That Affect Post-Graduation Satisfaction Of Chilean University Students’, Studies In Higher Education, 44(6). Doi: 10.1080/03075079.2017.1407306.

Han, J., Kamber, M. And Pei, J. (2012) Data Mining: Concepts And Techniques, Data Mining: Concepts And Techniques. Doi: 10.1016/C2009-0-61819-5.

Hand, D. J. (2008) ‘Data Mining: Methods And Models By D. T. Larose’, Biometrics, 64(1). Doi: 10.1111/J.1541-0420.2008.00962_9.X.

Hendley, K. (2021) ‘Rethinking The Role Of Personal Connections In The Russian Labor Market: Getting A Job As A Law Graduate In Russia’, Post-Soviet Affairs, 37(3). Doi: 10.1080/1060586X.2021.1874768.

Lagman, A. C. Et Al. (2019) ‘Embedding Naïve Bayes Algorithm Data Model In Predicting Student Graduation’, In Pervasivehealth: Pervasive Computing Technologies For Healthcare. Doi: 10.1145/3369555.3369570.

Lagman, A. C. Et Al. (2020) ‘Classification Algorithm Accuracy Improvement For Student Graduation Prediction Using Ensemble Model’, International Journal Of Information And Education Technology, 10(10). Doi: 10.18178/Ijiet.2020.10.10.1449.

Marzuqi, A., Laksitowening, K. A. And Asror, I. (2021) ‘Temporal Prediction On Students’ Graduation Using Naïve Bayes And K-Nearest Neighbor Algorithm’, JURNAL MEDIA INFORMATIKA BUDIDARMA, 5(2). Doi: 10.30865/Mib.V5i2.2919.

Mulia, I. And Muanas, M. (2021) ‘Model Prediksi Kelulusan Mahasiswa Menggunakan Decision Tree C4.5 Dan Software Weka’, JAS-PT (Jurnal Analisis Sistem Pendidikan Tinggi Indonesia), 5(1). Doi: 10.36339/Jaspt.V5i1.417.

N.Undavia, J., M. Dolia, P. And P. Shah, N. (2013) ‘Prediction Of Graduate Students For Master Degree Based On Their Past Performance Using Decision Tree In Weka Environment’, International Journal Of Computer Applications, 74(11). Doi: 10.5120/12930-9877.

Olalekan, A. M., Egwuche, O. S. And Olatunji, S. O. (2020) ‘Performance Evaluation Of Machine Learning Techniques For Prediction Of Graduating Students In Tertiary Institution’, In 2020 International Conference In Mathematics, Computer Engineering And Computer Science, ICMCECS 2020. Doi: 10.1109/ICMCECS47690.2020.240888.

Qin, L. And Phillips, G. A. (2019) ‘The Best Three Years Of Your Life: A Prediction Of Three-Year Graduation With Diagnostic Classification Model’, International Journal Of Higher Education, 8(6). Doi: 10.5430/Ijhe.V8n6p231.

Ristoski, P., Bizer, C. And Paulheim, H. (2015) ‘Mining The Web Of Linked Data With Rapidminer’, Journal Of Web Semantics, 35. Doi: 10.1016/J.Websem.2015.06.004.

Romadhona, A., Suprapedi And Himawan, H. (2017) ‘Prediksi Kelulusan Mahasiswa Tepat Waktu Berdasarkan Usia, Jenis Kelamin, Dan Indeks Prestasi Menggunakan Algoritma Decision Tree’, Jurnal Teknologi Informasi, 13.

Roy, S. Et Al. (2019) ‘Dispersion Ratio Based Decision Tree Model For Classification’, Expert Systems With Applications, 116. Doi: 10.1016/J.Eswa.2018.08.039.

Sembiring, M. T. And Tambunan, R. H. (2021) ‘Analysis Of Graduation Prediction On Time Based On Student Academic Performance Using The Naïve Bayes Algorithm With Data Mining Implementation (Case Study: Department Of Industrial Engineering USU)’, IOP Conference Series: Materials Science And Engineering, 1122(1). Doi: 10.1088/1757-899x/1122/1/012069.

Srinadi, I. G. A. M. And Nilakusmawati, D. P. E. (2020) ‘Analisis Waktu Kelulusan Mahasiswa Fmipa Universitas Udayana Dan Faktor-Faktor Yang Memengaruhinya’, E-Jurnal Matematika, 9(3). Doi: 10.24843/Mtk.2020.V09.I03.P300.

Sutoyo, E. And Almaarif, A. (2020) ‘Educational Data Mining For Predicting Student Graduation Using The Naïve Bayes Classifier Algorithm’, Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 4(1). Doi: 10.29207/Resti.V4i1.1502.

Undavia, J. N., Dolia, P. And Patel, A. (2016) ‘Customized Prediction Model To Predict Post-Graduation Course For Graduating Students Using Decision Tree Classifier’, Indian Journal Of Science And Technology, 9(12). Doi: 10.17485/Ijst/2016/V9i12/83335.


  • There are currently no refbacks.

Copyright (c) 2022 JMSP (Jurnal Manajemen dan Supervisi Pendidikan)

Hasil gambar untuk logo zotero ukuran kecil

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

View My Stats