Forecasting the Number of Students in Multiple Linear Regressions
DOI:
https://doi.org/10.30812/matrik.v21i2.1348Keywords:
Big data, Data Mining, Multiple linear regressions, ForecastingAbstract
The most important element of higher education was students, therefore every university must continue to improve services in the future, and one of them was by using decision support. This case could be done by utilizing the University of Big Data. Predicting the number of prospective students in higher education was done by utilizing data mining and multiple linear regression approaches. By using 2 independent variables, namely administration costs (X1), accreditation score (X2), and the number of students who was registered each year as dependent variable (Y). For the test data, it used database for the last 13 years. By using multiple linear regression, the intercept value was sought and the coefficient of determination until the regression coefficient was obtained with the equation Y = 45.28 + -0.02.X1 + 121.58.X2, noted that if X2 was constant, the increasing of one unit was in X1 would have the effect of increasing -0.02 units on Y. Secondly, if X1 was constant, the increasing of one unit was in X2, would have the effect of increasing 121.58 units in Y. Thirdly, if X1 and X2 were equal to zero, the magnitude of Y was 45.28 units. Therefore, the proposed approach could be provided the acceptable predictive results.
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References
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Published
2022-03-31
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Section
Articles
How to Cite
[1]
F. Riandari, H. T. Sihotang, and H. Husain, “Forecasting the Number of Students in Multiple Linear Regressions”, MATRIK, vol. 21, no. 2, pp. 249–256, Mar. 2022, doi: 10.30812/matrik.v21i2.1348.
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