Research Article Open Access

Big Data in Educational Institutions using RapidMiner to Predict Learning Effectiveness

Evaristus Didik Madyatmadja1, David Jumpa Malem Sembiring2, Sinek Mehuli Br Perangin Angin2, David Ferdy3 and Johanes Fernandes Andry3
  • 1 Bina Nusantara University, Indonesia
  • 2 Institut Teknologi dan Bisnis Indonesia, Indonesia
  • 3 Bunda Mulia University, Indonesia
Journal of Computer Science
Volume 17 No. 4, 2021, 403-413

DOI: https://doi.org/10.3844/jcssp.2021.403.413

Submitted On: 5 February 2021 Published On: 29 April 2021

How to Cite: Madyatmadja, E. D., Sembiring, D. J. M., Angin, S. M. B. P., Ferdy, D. & Andry, J. F. (2021). Big Data in Educational Institutions using RapidMiner to Predict Learning Effectiveness. Journal of Computer Science, 17(4), 403-413. https://doi.org/10.3844/jcssp.2021.403.413

Abstract

Schools, universities, colleges and other educational institutions store a lot of data related to students and teachers as well as those related to education. You can analyze this data to gain insights that can improve the operational efficiency of your organization. Educational institution's needs based on student behavior, student exam results, each student's growth and changed educational regulations can be handled through statistical analysis. Big Data asphalt the way for innovative systems that students learn in exciting ways. The use of Learning Management Systems gave birth to a data explosion in higher education which became an innovation for the world of education, namely Big Data. These large amounts of digital data will provide information on what students and student behavior and involvement, their assessment, motivation and preferences, thus providing a large amount of data that can be mined for the learning experience. By utilizing the results of Big Data analysis in tertiary institutions, it can be obtained more insight into students, academics and the process in higher education so that it supports predictive analysis and increases decision making based on data which in turn can help improve the successful performance of students and institutions. This study using secondary data collection methods from journals or books. Method used is an analysis using the Decision Tree, Naive Bayes and K-Means. These results can predict the value obtained by students Learning Effectiveness in an educational institution, then a solution can be searched to increase the value of students in that institution.

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Keywords

  • Big Data
  • Educational
  • Institutions
  • RapidMiner
  • Predict Learning
  • Decision Tree