Grouping Student Awareness on Security Of E-Learning Information Using Fuzzy C-Means Method

Abstract

Many educational institutions have been forced to adapt how they present the teaching and learning process, including the creation of appropriate learning media due to the current Covid-19 pandemic. This is accomplished through the development of an integrated online learning system known as E-Learning. Aside from all of the benefits and positive outcomes that E-Learning can give, there are also drawbacks to student information security, such as assignment theft, piracy of E-Learning, the misuse of passwords by irresponsible students, and other problems. To anticipate this, the researcher intended to group students' awareness of their respective E-Learning information security by using the Fuzzy C Means method. Fuzzy C Means uses a fuzzy grouping model so that data can be members of all classes or clusters formed with different degrees or levels of membership between 0 to 1. The sample used to represent the population is 20 students of STIKOM PGRI Banyuwangi, Indonesia. The results obtained are to find out how well the grouping of student awareness clusters on E-Learning information security. There are 3 clusters of student E-Learning information security awareness. Cluster 1 consists of students with high awareness, cluster 2 contains categories of students with low awareness, and the third cluster consists of students with moderate awareness.