SCHOOL SUCCESS PREDICTION USING ARTIFICIAL NEURAL NETWORK BASED ON INTERNAL AND EXTERNAL FACTORS

Mahaputra Hidayat, Ratna Nur Tiara Shanty

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In relation with improving the quality of education, various attempts have been made: an increase teachers quality, complete educational facilities, increased allocation of funds for education and educational evaluation of the implementation of sustainable activities. Once observed, it seems clear that the problem is serious in improving the quality of education is the low quality of education at all educational levels, especially schools. Evaluation aims to assess the failure of schools achieving good standards of competence in effort schools improve the quality of education. Levels of school failure to improve the quality of education is influenced by several factors both from the students and of teachers and the school itself. With artificial neural network (ANN), we expect that we will be able to predict school failure which is related to several internal and external factors. So that, we can obtain valid information about some attributes which are affecting to the school failure. Then, some actions can be taken for preventing school failure as the effort to increase those school’s educational quality. From the experimental results yield the number of hidden nodes configuration 10, the value of learning rate 0.15, momentum 0.6 and the tolerance value of MSE 0.0013118%.

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Araque F., Roldan C., Salguero A. (2009). Factors Influencing University Drop Out Rates. Computers & Education, 53, 563–574.

Croninger, Robert., King Rice, Jennifer., Rathbun, Am., Nishio, Masako. (2007). Teacher qualifications and early learning: Effects of certification, degree, and experience on first-grade student achievement. Journal of Sciencedirect, 26, 312-324.

H. Trias Rahmadya and W. Pudjo Prabowo, (2009). Penerapan Soft Computing Dengan Matlab. Rekayasa Sains.

Kotsiantis S. (2009). Educational Data Mining: A Case Study for Predicting Dropout – Prone Students. Int. J. Knowledge Engineering and Soft Data Paradigms, 1(2), 101–111.

Lipsitz, J. (1984). Successful schools for young adolescents. New Brunswick, NJ: Transaction.

Olsen J., Aleven V. and Rummel N. (2015). Predicting Student Performance In a Collaborative Learning Environment.Proceedings of the 8th International Conference on Data Mining, 211-217.

Vera, C. Márquez., Romero, C., Ventura, S. (2011).Predicting School Failure Using Data Mining. Proceedings of the Fourth International Conference on Data Mining, 271-275.


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