Analysis Of Multiple Regression Data Mining Methods On The Prediction Of Ibtidaiyah School Registration
Keywords:Multiple Linear Regression, Data Mining, Registrant, Data Explosion
Data mining originates from data explosion problems experienced by agencies / companies that have collected data from various kinds of transactions. Data mining is the process of looking for patterns or interesting information in selected data using certain techniques or methods. Techniques, methods, or algorithms in data mining vary widely. The choice of the right method or algorithm is very much dependent on the objectives and the overall Knowledge Discovery in Database (KDD) process. The algorithm used in this research is Multiple Linear Regression. School is a suitable place for the application of this method, therefore this research was conducted at the Madrasah Ibtidaiyah Sinaksak Foundation School. The purpose of this study, among others, was to determine the number of registrants at the Madrasah Ibtidaiyah Foundation School (YMI) Sinaksak. In this study, researchers used multiple linear regression association data mining methods. Sources of research data used are observation and interview methods. It is hoped that from the research the school can make a decision or strategy in the estimation of registrants in the following year.
M. Sarstedt and E. Mooi, “Regression Analysis,” 2014, pp. 193–233.
J. Majumdar, S. Naraseeyappa, and S. Ankalaki, “Analysis of agriculture data using data mining techniques: application of big data,” J. Big Data, vol. 4, no. 1, p. 20, 2017, doi: 10.1186/s40537-017-0077-4.
D. Abdullah et al., “Data Mining to Determine Correlation of Purchasing Cosmetics with A priori Method,” in Journal of Physics: Conference Series, 2019, vol. 1361, no. 1, doi: 10.1088/1742-6596/1361/1/012056.
F. Maksood and G. Achuthan, “Analysis of Data Mining Techniques and its Applications,” Int. J. Comput. Appl., vol. 140, pp. 6–14, Apr. 2016, doi: 10.5120/ijca2016909249.
N. Khan et al., “Big Data: Survey, Technologies, Opportunities, and Challenges,” Sci. World J., vol. 2014, p. 712826, 2014, doi: 10.1155/2014/712826.
I. A. Ajah and H. F. Nweke, “Big Data and Business Analytics: Trends, Platforms, Success Factors and Applications,” Big Data and Cognitive Computing , vol. 3, no. 2. 2019, doi: 10.3390/bdcc3020032.
W. He, Z. J. Zhang, and W. Li, “Information technology solutions, challenges, and suggestions for tackling the COVID-19 pandemic,” Int. J. Inf. Manage., vol. 57, p. 102287, Apr. 2021, doi: 10.1016/j.ijinfomgt.2020.102287.
A. M. H. Pardede et al., “Application of Data Mining Prediction of Electricity Deviation Flow Using Metode Backpropogation at PLN Binjai Area,” in Journal of Physics: Conference Series, 2019, vol. 1363, no. 1, doi: 10.1088/1742-6596/1363/1/012067.
O. Aissaoui, Y. Madani, L. Oughdir, A. Dakkak, and Y. EL ALLIOUI, “A Multiple Linear Regression-Based Approach to Predict Student Performance,” 2020, pp. 9–23.
Z. Ismail, A. Yahya, and A. Shabri, “Forecasting Gold Prices Using Multiple Linear Regression Method Department of Mathematics , Faculty of Science Department of Basic Education , Faculty of Education,” Am. J. Appl. Sci., vol. 6, no. 8, pp. 1509–1514, 2009.
U. Khair, H. Fahmi, S. Al Hakim, and R. Rahim, “Forecasting Error Calculation with Mean Absolute Deviation and Mean Absolute Percentage Error,” J. Phys. Conf. Ser., vol. 930, no. 1, 2017, doi: 10.1088/1742-6596/930/1/012002.
E. C. Alexopoulos, “Introduction to multivariate regression analysis,” Hippokratia, vol. 14, no. Suppl 1, pp. 23–28, Dec. 2010.
How to Cite
Copyright (c) 2022 Journal of Artificial Intelligence and Engineering Applications (JAIEA)
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.