COMPARATIVE STUDY OF MACHINE LEARNING KNN, SVM, AND DECISION TREE ALGORITHM TO PREDICT STUDENT’S PERFORMANCE
DOI:
https://doi.org/10.29121/granthaalayah.v7.i1.2019.1048Keywords:
Student Performance, KNN, SVM, Decision TreeAbstract [English]
Students who are not-active will affect the number of students who graduate on time. Prevention of not-active students can be done by predicting student performance. The study was conducted by comparing the KNN, SVM, and Decision Tree algorithms to get the best predictive model. The model making process was carried out by steps; data collecting, pre-processing, model building, comparison of models, and evaluation. The results show that the SVM algorithm has the best accuracy in predicting with a precision value of 95%. The Decision Tree algorithm has a prediction accuracy of 93% and the KNN algorithm has a prediction accuracy value of 92%.
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BAN-PT, Buku I Naskah Akademik Akreditasi Institusi Perguruan Tinggi. Jakarta: BAN-PT, 2011.
C. L. Sa, D. H. b. A. Ibrahim, E. D. Hossain, and M. bin Hossin, “Student performance analysis system (SPAS),” in The 5th International Conference on Information and Communication Technology for The Muslim World (ICT4M), 2014, pp. 1–6.
I. Singh, A. S. Sabitha, and A. Bansal, “Student performance analysis using clustering algorithm,” in 2016 6th International Conference - Cloud System and Big Data Engineering (Confluence), 2016, pp. 294–299. DOI: https://doi.org/10.1109/CONFLUENCE.2016.7508131
T. Devasia, T. P. Vinushree, and V. Hegde, “Prediction of students performance using Educational Data Mining,” in 2016 International Conference on Data Mining and Advanced Computing (SAPIENCE), 2016, pp. 91–95. DOI: https://doi.org/10.1109/SAPIENCE.2016.7684167
M. N. Quadri and N. V. Kalyankar, “Drop Out Feature of Student Data for Academic Performance Using Decision Tree Techniques,” Glob. J. Comput. Sci. Technol., vol. 10, no. 2, pp. 2–5, 2010.
R. Asif, A. Merceron, S. A. Ali, and N. G. Haider, “Analyzing undergraduate students’ performance using educational data mining,” Comput. Educ., vol. 113, pp. 177–194, 2017. DOI: https://doi.org/10.1016/j.compedu.2017.05.007
A. Vihavainen, “Predicting Students’ Performance in an Introductory Programming Course Using Data from Students’ Own Programming Process,” in 2013 IEEE 13th International Conference on Advanced Learning Technologies, 2013, pp. 498–499. DOI: https://doi.org/10.1109/ICALT.2013.161
G. Kostopoulos and A. Lipitakis, “Predicting Student Performance in Distance Higher Education Using Active Learning,” Int. Conf. Eng. Appl. Neural Networks, vol. 744, no. Dm, pp. 75–86, 2017. DOI: https://doi.org/10.1007/978-3-319-65172-9_7
R. Conijn, A. Van den Beemt, and P. Cuijpers, “Predicting student performance in a blended MOOC,” J. Comput. Assist. Learn., no. March, pp. 1–14, 2018. DOI: https://doi.org/10.1111/jcal.12270
A. Daud, N. R. Aljohani, R. A. Abbasi, M. D. Lytras, F. Abbas, and J. S. Alowibdi, “Predicting Student Performance using Advanced Learning Analytics,” Proc. 26th Int. Conf. World Wide Web Companion, pp. 415–421, 2017. DOI: https://doi.org/10.1145/3041021.3054164
Z. Yıldız and A. F. Baba, “Evaluation of student performance in laboratory applications using fuzzy decision support system model,” in 2014 IEEE Global Engineering Education Conference (EDUCON), 2014, pp. 1023–1027. DOI: https://doi.org/10.1109/EDUCON.2014.6826230
P. Nagar, “Application of Fuzzy Logic for Evaluation of Academic Performance of Students of Computer,” Iran. Conf. Fuzzy Syst., vol. 3, no. X, pp. 260–267, 2013.
E. Rainarli and A. Romadhan, “Perbandingan Simple Logistic Classifier dengan Support Vector Machine dalam Memprediksi Kemenangan Atlet,” J. Inf. Syst. Eng. Bus. Intell., vol. 3, no. 2, p. 87, 2017. DOI: https://doi.org/10.20473/jisebi.3.2.87-91
Y. Paul, V. Goyal, and R. A. Jaswal, “Comparative analysis between SVM & KNN classifier for EMG signal classification on elementary time domain features,” 4th IEEE Int. Conf. Signal Process. Comput. Control. ISPCC 2017, vol. 2017–Janua, pp. 169–175, 2018.
R. A. Nugraheni and K. Mutijarsa, “Comparative Analysis of Machine Learning KNN, SVM, and Random Forests Algorithm for Facial Expression Classification,” in ISEMANTIC, 2016, pp. 163–168. DOI: https://doi.org/10.1109/ISEMANTIC.2016.7873831
V. Patil, S. Suryawanshi, M. Saner, and V. Patil, “Student Performance Prediction Using Classification Data Mining Techniques,” Int. J. Res. Emerg. Sci. Technol., vol. 4, no. 444, pp. 15–18, 2017.
A. U. Khasanah and Harwati, “A Comparative Study to Predict Student’s Performance Using Educational Data Mining Techniques,” IOP Conf. Ser. Mater. Sci. Eng., vol. 215, p. 012036, Jun. 2017.
G. S. Gowri, R. Thulasiram, and M. A. Baburao, “Educational Data Mining Application for Estimating Students Performance in Weka Environment,” IOP Conf. Ser. Mater. Sci. Eng., vol. 263, no. 3, 2017. DOI: https://doi.org/10.1088/1757-899X/263/3/032002
M. Ciolacu, A. F. Tehrani, R. Beer, and H. Popp, “Education 4. 0 – Fostering Student’ s Performance with Machine Learning Methods,” in SIITME, 2017. DOI: https://doi.org/10.1109/SIITME.2017.8259941
B. Lantz, Machine Learning with R, 2nd ed. Birmingham-Mumbai: Packt Publishing Ltd., 2015
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