Classification of Student Academic Performance using Fuzzy Soft Set

Abstract

Students are one of the substances that need to be considered in relation to the world of education, because students are translators of the dynamics of science, and carry out the task of exploring that knowledge. As a subject with potential and, at the same time, objects in their activities and creativity, students are expected to be able to develop their qualities. The quality can be seen from the academic achievements achieved, which are evidence of the effort earned by students. Student academic achievement is evaluated at the end of each semester to determine the learning outcomes that have been achieved. If a student cannot meet certain academic criteria to be declared eligible to continue their studies, the student is declared to be not graduating on time or even dropout (DO). The high number of students not graduating on time or dropouts at higher institutions can be minimized by the policies of higher institutions by directing and detecting at-risk students in the early stages of education. Therefore, in this paper, we present the use of Fuzzy Soft Set Classification (FSSC), which is based on the Fuzzy Soft set theory to predict student graduation. The 2068 dataset was taken from the Directorate of Information Systems, Ahmad Dahlan University. The results showed that the FSSC reached up to 0.893292 in terms of accuracy. So, it is expected to be able to detect students at risk in the early stages of education so that higher education can minimize students not graduating on time or dropout by providing appropriate treatment and designing strategic programs.