Forecasting the Number of Students in Multiple Linear Regressions

  • Fristi Riandari STMIK Pelita Nusantara
  • Hengki Tamando Sihotang STMIK Pelita Nusantara
  • Husain Husain Universitas Bumigora
Keywords: Big data, Data Mining, Multiple linear regressions, Forecasting


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.


Download data is not yet available.


[1] L. Ardito, R. Cerchione, P. Del Vecchio, and E. Raguseo, “Big data in smart tourism: challenges, issues and opportunities,” Curr. Issues Tour., vol. 22, no. 15, pp. 1805–1809, 2019.
[2] B. Furht and F. Villanustre, “Big data technologies and applications,” Big Data Technol. Appl., pp. 1–400, 2016.
[3] R. Dautov and S. Distefano, “Quantifying volume, velocity, and variety to support (Big) data-intensive application development,” Proc. - 2017 IEEE Int. Conf. Big Data, Big Data 2017, vol. 2018-January, pp. 2843–2852, 2017.
[4] I. A. T. Hashem et al., “The role of big data in smart city,” Int. J. Inf. Manage., vol. 36, no. 5, pp. 748–758, 2016.
[5] T. M. Song and J. Song, “Prediction of risk factors of cyberbullying-related words in Korea: Application of data mining using social big data,” Telemat. Informatics, vol. 58, p. 101524, 2021.
[6] T. Gajdošík, “Big Data Analytics in Smart Tourism Destinations. A New Tool for Destination Management Organizations?,” pp. 15–33, 2019.
[7] A. Gandomi and M. Haider, “Beyond the hype: Big data concepts, methods, and analytics,” Int. J. Inf. Manage., vol. 35, no. 2, pp. 137–144, 2015.
[8] D. Wang, X. Robert, and Y. Li, “China’s ‘Smart Tourism Destination’ Initiative : A Taste Of the Service-Dominant Logic,” J. Destin. Mark. Manag., vol. 2, no. 2, pp. 59–61, 2013.
[9] A. Yang, Y. Han, C.-S. Liu, J.-H. Wu, and D.-B. Hua, “D-TSVR Recurrence Prediction Driven by Medical Big Data in Cancer,” IEEE Trans. Ind. Informatics, vol. 3203, no. c, pp. 1–1, 2020.
[10] A. Dridi, M. M. Gaber, R. M. A. Azad, and J. Bhogal, “Scholarly data mining: A systematic review of its applications,” Wiley Interdiscip. Rev. Data Min. Knowl. Discov., no. October, pp. 1–23, 2020.
[11] Y. Ge and H. Wu, “Prediction of corn price fluctuation based on multiple linear regression analysis model under big data,” Neural Comput. Appl., vol. 32, no. 22, pp. 16843–16855, 2020.
[12] J. Hong, Z. Wang, W. Chen, L. Y. Wang, and C. Qu, “Online joint-prediction of multi-forward-step battery SOC using LSTM neural networks and multiple linear regression for real-world electric vehicles,” J. Energy Storage, vol. 30, no. February, p. 101459, 2020.
[13] K. L. L. Khine and T. T. S. Nyunt, Predictive big data analytics using multiple linear regression model, vol. 744. Springer Singapore, 2019.
[14] X. Xu, Z. Sun, L. Wang, J. Fu, and C. Wang, “A Comparative Study of Customer Complaint Prediction Model of Time Series, Multiple Linear Regression and BP Neural Network,” J. Phys. Conf. Ser., vol. 1187, no. 5, 2019.
[15] F. Wang, Z. Shi, A. Biswas, S. Yang, and J. Ding, “Multi-algorithm comparison for predicting soil salinity,” Geoderma, vol. 365, no. February 2019, p. 114211, 2020.
[16] H. Rawashdeh et al., “Intelligent system based on data mining techniques for prediction of preterm birth for women with cervical cerclage,” Comput. Biol. Chem., vol. 85, no. February, p. 107233, 2020.
[17] Y. S. Lee, J. R. Wang, J. W. Zhan, and J. M. Zhang, “Data Mining Analysis of Overall Team Information Based on Internet of Things,” IEEE Access, vol. 8, pp. 41822–41829, 2020.
[18] C. N. Burger, T. L. Grobler, and W. Kleynhans, “Discrete Kalman Filter and Linear Regression Comparison for Vessel Coordinate Prediction,” Proc. - IEEE Int. Conf. Mob. Data Manag., vol. 2020-June, no. Mdm, pp. 269–274, 2020.
[19] Y. S. Kong, S. Abdullah, D. Schramm, M. Z. Omar, and S. M. Haris, “Development of multiple linear regression-based models for fatigue life evaluation of automotive coil springs,” Mech. Syst. Signal Process., vol. 118, pp. 675–695, 2019.
[20] Bochumer Institut für Technologie GmbH, Data Science - Data Science, no. September 2016. 2018.
[21] Liu, C., Jin, R., Gong, E., Liu, Y., Yue, M., “Prediction for the Performance of Gas Turbine Units Using Multiple Linear Regression,”Proc.- Of the Chinese Society of Electrical Engineering., vol. 37, pp. 4731-4738, Aug 2017.
[22] X. Li, H. Dong, and S. Han, “Multiple Linear Regression with Kalman Filter for Predicting End Prices of Online Auctions,” 2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech), Aug. 2020.
How to Cite
Riandari, F., Sihotang, H., & Husain, H. (2022). Forecasting the Number of Students in Multiple Linear Regressions. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 21(2), 249-256.