Seleksi Fitur dan Perbandingan Algoritma Klasifikasi untuk Prediksi Kelulusan Mahasiswa

Junta Zeniarja, Abu Salam, Farda Alan Ma'ruf


Students are a major part of the life cycle of a university. The number of students graduating from a university often has a small ratio when compared to the number of students obtained in the same academic year. This small student graduation rate can be caused by several aspects, such as the many student activities accompanied by economic aspects, as well as other aspects. This makes it mandatory for a university to have a model that can take into account whether the student can graduate on time or not. One of the main factors that determine the reputation of a university is student graduation on time. The higher the level of new students at a university, with the same ratio, there must also be students who graduate on time. An increase in the number of student data and academic data occurs if many students do not graduate on time from all registered students. So that it will affect the image and reputation of the university which can later threaten the accreditation value of the university. To overcome this, we need a model that can predict student graduation so that it can be used as policy making later. The purpose of this study is to propose the best classification model by comparing the highest level of accuracy of several classification algorithms including Naïve Bayes, Random Forest, Decision Tree, K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) to predict student graduation. In addition, the feature selection process is also used before the classification process to optimize the model. The use of feature selection in this model with the best features using 12 regular attribute features and 1 attribute as a label. It was found that the classification model using the Random Forest algorithm was chosen, with the highest accuracy value reaching 77.35% better than other algorithms.


Feature selection; classification; graduation; student; random forest

Full Text:



A. Tekin, “Early Prediction of Students’ Grade Point Averages at Graduation: A Data Mining Approach,” Eurasian J. Educ. Res., vol. 14, no. 54, pp. 207–226, 2014.

A. M. Shahiri, W. Husain, and N. A. Rashid, “A Review on Predicting Student’s Performance Using Data Mining Techniques,” in Procedia Computer Science, 2015, vol. 72, pp. 414–422.

E. Purnamasari, D. P. Rini, and Sukemi, “The Combination of Naive Bayes and Particle Swarm Optimization Methods of Student’s Graduation Prediction,” J. Ilm. Tek. Elektro Komput. dan Inform., vol. 5, no. 2, pp. 112–119, 2019.

M. Babar et al., “Heliyon Psychological impacts of COVID-19 and satisfaction from online classes : disturbance in daily routine and prevalence of depression , stress , and anxiety among students of Pakistan,” vol. 7, no. October 2020, 2021.

T. A. Birtch, F. F. T. Chiang, Z. Cai, and J. Wang, “Am I choosing the right career? The implications of COVID-19 on the occupational attitudes of hospitality management students,” Int. J. Hosp. Manag., vol. 95, no. March, p. 102931, 2021.

M. K. Prof. Dr. Ir. Edi Noersasongko, “Greetings from our Rector,” 2020. [Online]. Available: [Accessed: 20-May-2021].

A. Luthfiarta, J. Zeniarja, E. Faisal, and W. Wicaksono, “Prediction on Deposit Subscription of Customer based on Bank Telemarketing using Decision Tree with Entropy Comparison,” J. Appl. Intell. Syst., vol. 4, no. 2, pp. 57–66, 2019.

J. Zeniarja, K. Widia, and R. R. Sani, “Penerapan Algoritma Naive Bayes dan Forward Selection dalam Pengklasifikasian Status Gizi Stunting pada Puskesmas Pandanaran Semarang,” JOINS (Journal Inf. Syst., vol. 5, no. 1, pp. 1–9, 2020.

A. S. H. Basari, B. Hussin, I. G. P. Ananta, and J. Zeniarja, “Opinion mining of movie review using hybrid method of support vector machine and particle swarm optimization,” Procedia Eng., vol. 53, pp. 453–462, 2013.

A. Khalaf Hamoud et al., “Supervised Learning Algorithms in Educational Data Mining: A Systematic Review,” Southeast Eur. J. Soft Comput., vol. 10, no. 1, pp. 55–70, 2021.

A. K. Das and E. Rodriguez-Marek, “A predictive analytics system for forecasting student academic performance: Insights from a pilot project at eastern Washington university,” 2019 Jt. 8th Int. Conf. Informatics, Electron. Vision, ICIEV 2019 3rd Int. Conf. Imaging, Vis. Pattern Recognition, icIVPR 2019 with Int. Conf. Act. Behav. Comput. ABC 2019, pp. 255–262, 2019.

G. A. Agarkov, A. A. Tarasyev, and A. D. Sushchenko, “Optimization of Students’ Graduation by the University Taking into Account the Needs of the Labor Market,” IFAC-PapersOnLine, vol. 53, no. 2, pp. 17399–17404, 2020.

A. Gonzalez-Nucamendi, J. Noguez, L. Neri, V. Robledo-Rella, R. M. G. García-Castelán, and D. Escobar-Castillejos, “The prediction of academic performance using engineering student’s profiles,” Comput. Electr. Eng., vol. 93, no. August 2020, p. 107288, 2021.

X. Lu, Y. Zhu, Y. Xu, and J. Yu, “Learning from multiple dynamic graphs of student and course interactions for student grade predictions,” Neurocomputing, vol. 431, pp. 23–33, 2021.

X. Wang, C. Zhou, and X. Xu, “Application of C4.5 decision tree for scholarship evaluations,” Procedia Comput. Sci., vol. 151, no. 2018, pp. 179–184, 2019.

F. M. Almutairi, N. D. Sidiropoulos, and G. Karypis, “Context-Aware Recommendation-Based Learning Analytics Using Tensor and Coupled Matrix Factorization,” IEEE J. Sel. Top. Signal Process., vol. 11, no. 5, pp. 729–741, 2017.

B. Albreiki, N. Zaki, and H. Alashwal, “A Systematic Literature Review of Student’ Performance Prediction Using Machine Learning Techniques,” Educ. Sci., vol. 11, no. 9, 2021.

A. M. Olalekan, O. S. Egwuche, and S. O. Olatunji, “Performance Evaluation of Machine Learning Techniques for Prediction of Graduating Students in Tertiary Institution,” 2020 Int. Conf. Math. Comput. Eng. Comput. Sci. ICMCECS 2020, 2020.

S. Alturki, I. Hulpuș, and H. Stuckenschmidt, Predicting Academic Outcomes: A Survey from 2007 Till 2018, no. 0123456789. Springer Netherlands, 2020.

X. Bai et al., “Educational Big Data: Predictions, Applications and Challenges,” Big Data Res., vol. 26, p. 100270, 2021.

T. F. Prasetyo, D. Susandi, and I. S. Widianingrum, “Prediksi Kelulusan Mahasiswa Pada Perguruan Tinggi Kabupaten Majalengka Berbasis Knowledge Based System,” Semin. Nas. Telekomun. dan Inform. | vol | issue | 2016, no. November, 2016.

E. Sutoyo and A. Almaarif, “Educational Data Mining untuk Prediksi Kelulusan Mahasiswa Menggunakan Algoritme Naïve Bayes Classifier,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 4, no. 1, pp. 95–101, 2020.

Y. P. Chiu, “Social recommendations for facebook brand pages,” J. Theor. Appl. Electron. Commer. Res., vol. 16, no. 1, pp. 71–84, 2020.

W. Wiguna and D. Riana, “Diagnosis of Coronavirus Disease 2019 (Covid-19) Surveillance Using C4.5 Algorithm,” J. Pilar Nusa Mandiri, vol. 16, no. 1, pp. 71–80, 2020.

T. Hardiani, “Comparison of Naive Bayes Method, K-NN (K-Nearest Neighbor) and Decision Tree for Predicting the Graduation of ‘Aisyiyah University Students of Yogyakarta,” Int. J. Heal. Sci. Technol., vol. 2, no. 1, 2021.

Y. Long, J. Liu, M. Fang, T. Wang, and W. Jiang, “Prediction of employee promotion based on personal basic features and post features,” ACM Int. Conf. Proceeding Ser., pp. 5–10, 2018.

Y. Nieto, V. Gacia-Diaz, C. Montenegro, C. C. Gonzalez, and R. Gonzalez Crespo, “Usage of Machine Learning for Strategic Decision Making at Higher Educational Institutions,” IEEE Access, vol. 7, pp. 75007–75017, 2019.

B. A. Arifiyani and R. S. Samosir, “Sistem Simulasi Prediksi Profil Kelulusan Mahasiswa Dengan Decison Tree,” J. Sains dan Teknol., vol. 5, no. 2, pp. 115–123, 2018.

L. A. R. Hakim, A. A. Rizal, and D. Ratnasari, “Aplikasi Prediksi Kelulusan Mahasiswa Berbasis K-Nearest Neighbor (K-NN),” JTIM J. Teknol. Inf. dan Multimed., vol. 1, no. 1, pp. 30–36, 2019.

P. S. C. Moonallika, K. Q. Fredlina, and I. B. K. Sudiatmika, “Penerapan Data Mining Untuk Memprediksi Kelulusan Mahasiswa Menggunakan Algoritma Naive Bayes Classifier (Studi Kasus STMIK Primakara),” J. Ilm. Komput., vol. 6, no. 1, pp. 47–56, 2020.

E. Haryatmi and S. P. Hervianti, “Penerapan Algoritma Support Vector Machine Untuk Model Prediksi Kelulusan Mahasiswa Tepat Waktu,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 2, pp. 386–392, 2021.

I. P. Astuti, “Prediksi Ketepatan Waktu Kelulusan Dengan Algoritma Data Mining C4.5,” Fountain Informatics J., vol. 2, no. 2, p. 5, 2017.

L. O. M. Zulfiqar, N. Renaningtias, and M. Y. Fathoni, “Educational Data Mining in Graduation Rate and Grade Predictions Utilizing Hybrid Decision Tree and Naïve Bayes Classifier,” no. Conrist 2019, pp. 151–157, 2020.

M. Munawir and T. Iqbal, “Prediksi Kelulusan Mahasiswa menggunakan Algoritma Naive Bayes (Studi Kasus 5 PTS di Banda Aceh),” J. JTIK (Jurnal Teknol. Inf. dan Komunikasi), vol. 3, no. 2, p. 59, 2019.

F. Gorunescu, Data Mining - Concepts, Models and Techniques, vol. 12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.



  • There are currently no refbacks.

View My Stats


Creative Commons License

Jurnal Rekayasa Elektrika (JRE) is published under license of Creative Commons Attribution-ShareAlike 4.0 International License.