Cosmas Haryawan, Maria Mediatrix Sebatubun


University is one of the educational institutions and can be established by the government or the individual. At this time, Indonesia has hundreds of universities spread throughout the region. As an educational institution, university of course must be able to educate its students and issue quality graduates with the academically and non-academically qualified. In its implementation, there are many problems that should be resolved as well as possible, such as when there are students who intentionally stop or disappear before completing their education or are even unable to complete their education and issued by institution (dropout).

Based on these problems, this research makes a model for predicting students who have the potential to fail or dropout during their studies using one of the data mining methods namely Multilayer Perceptron by referring to personal and academic data. The results obtained from this research are 86.9% an accuracy rate with the 54.7% sensitivity, and 95.4% specificity. This research is expected to be used to determine the need strategies to minimize the number of students who stop or dropout.

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J. P. Jiawei Han, Micheline Kamber, Data Mining – Concepts & Techniques. 2011.

A. Sangodiah and B. Balakrishnan, "Holistic Prediction of Student Attrition in Higher Learning Institutions in Malaysia Using Support Vector Machine Model," Int. J. Res. Stud. Comput. Sci. Eng., vol. 1, no. 1, pp. 29–35, 2014.

L. P. Khobragade and P. P. Mahadik, "Students ’ Academic Failure Prediction Using Data Mining," Int. J. Adv. Res. Comput. Commun. Eng., vol. 4, no. 11, pp. 290–298, 2015.

S. Sultana, S. Khan, and M. A. Abbas, "Predicting performance of electrical engineering students using cognitive and non-cognitive features for identification of potential dropouts," Int. J. Electr. Eng. Educ., vol. 54, no. 2, pp. 105–118, 2017.

G. J. A. Baars, T. Stijnen, and T. A. W. Splinter, "A Model to Predict Student Failure in the First Year of the Undergraduate Medical Curriculum," Heal. Prof. Educ., vol. 3, no. 1, pp. 5–14, 2017.

M. Al, A. Tucker, and L. Yousefi, "The Prediction of Student Failure using Classification Methods : A Case Study," in Proc. Int. Conf. Image Process. Pattern Recognit., pp. 79–90, 2018.

I. made B. Adnyana, "Penerapan Feature Selection untuk Prediksi Lama Studi Mahasiswa," J. Sist. Dan Inform., vol. 13, pp. 72–76, 2019.

M. Negnevitsky, Artificial intelligence, vol. VII. 2001.

J. Han and M. Kamber, Data Mining: Concepts and Techniques, vol. 12. 2011.

L. Noriega, "Multilayer Perceptron Tutorial." 2005.

M. M. Sebatubun and M. A. Nugroho, "Ekstraksi Fitur Circularity untuk Pengenalan Varietas Kopi Arabika," J. Teknol. Inf. dan Ilmu Komput., vol. 4, no. 4, pp. 283–289, 2017.

A. G. Karegowda, A. S. Manjunath, and M. A. Jayaram, "Comparative Study of Attribute Selection using Gain Ratio and Correlation Based Feature Selection," Int. J. Inf. Technol. Knowl. Manag., vol. 2, no. 2, pp. 271–277, 2010.



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