The Use of Big Data Analytics in Detecting Academic Fraud

Abstract

This study aims to find out the effect of using big data analytics on the detection of academic fraud so that it can provide improvements and create significant changes, especially in reducing the level of academic fraud among students. The variables used in this research are big data analytics as the independent variable and academic fraud as the dependent variable. This study uses primary data obtained from ques-tionnaires distributed to Trilogy University students. The sample is 258 students from all study programs at Trilogy University class 2017 - 2020. The data processing and analysis method uses Partial Least Square (PLS). The results of this study indicate that the use of big data analytics has a positive and significant effect on the detection of academic fraud. This shows that universities that use big data analytics are able to detect academic fraud committed by students.