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A COMPARISON BETWEEN SIMPLE LINEAR REGRESSION AND RADIAL BASIS FUNCTION NEURAL NETWORK (RBFNN) MODELS FOR PREDICTING STUDENTS’ ACHIEVEMENT

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Date
2014
Author
Haviluddin, Haviluddin
Sunarto, Andang
Yuniarti, Suci
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Abstract
This paper presents an approach for predicting student achievements using statistics and artificial neural networks (ANN), namely linear regression and radial basis function neural network (RBFNN) methods. The data is gained from 108 students from mathematics department in Islamic University, Bengkulu, Indonesia. The results of measurement are then compared to the value of the mean of square error (MSE). The results show that MSE 0.076 with model Y = 3.193 + 0.002 for linear regression and MSE 0.003, model Y = (1)T + (0.0021) with sum-squared error goal 0.01, and spread 1 for the RBFNN. The smallest MSE value indicates a good method for accuracy. Therefore, the RBFNN model illustrates the proposed best model to predict students’ achievement.
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http://repository-ds.unmul.ac.id:8080/handle/123456789/966
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Repository Universitas Mulawarman copyright ©   LP3M Universitas Mulawarman
Jalan Kuaro Kotak Pos 1068
Telp. (0541) 741118
Fax. (0541) 747479 - 732870
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Contact Us | Send Feedback