Modeling Data Mining Algorithm for Predicting Timely Student Graduation at State University Surabaya

Abstract

Information technology aims to advance human activities. Even now it has penetrated into the field of education, as a process and industrial sector, education cannot be separated from the field of information and technology. In the implementation of the academic process, the State University of Surabaya, which has 30,000 students, faces several obstacles in predicting the students' graduation time on time. The information system owned has not been used optimally to predict the time of student graduation. It is difficult to predict because this university does not have an exact time prediction pattern to use as a basis for predicting the number of students who will graduate on time. In helping these problems, the researcher provides a solution, namely building a data mining algorithm model to predict the exact time of graduation for Surabaya State University students. The data used are Strata-1 (S1) PTI, SI and TI students from 2014-2018. The methodology in this study is FAST (Framework For The Application Of System Thinking) using the Neural Network (NN) algorithm 761 student data with the input value of the artificial neural network method 4, hidden layer 5 and output 2 providing an accuracy of 99.85%. Details of late predictions are 98.06% as many as 101 students and correct predictions are 99.85% as many as 657 students. Shows that the Neural Network can be used as a prediction of the graduation of Strata-1 (S1) PTI, SI, and IT students at the State University of Surabaya.