Graduation Prediction of S1 Industrial Engineering Students IST AKPRIND by Using Data Mining Method

Agus Hindarto Wibowo

doi:10.31940/logic.v20i1.1580

Abstract

There is data of students who experience Drop Out which raises the curiosity in IST AKPRIND's industrial engineering study program on students’ graduation patterns. It is necessary to have research on how to classify the data held by industrial engineering study programs in order to obtain students’ graduation patterns as evaluation material in the administration of study programs. This study also produced a design to set the goals of Educational Data Mining, this case as a student modeling that would be achieved by predicting using the Decision Tree method. The final results showed a mismatch between the general information data passed and the drop out of the rule obtained using the decision tree algorithm in the Rapidminer software which is shown by an accuracy of 95.83%. This value indicates that there is a match between the prediction of student identity data with the rule obtained using the decision tree algorithm.

Highlights

Students at the tertiary level of education whose rights are regulated in Law no. 20 of 2003 Chapter V concerning Students. universities should have students’ data stored in information systems
It is not easy to make predictions by utilizing various raw data held by the institute, so Educational Data Mining techniques are necessary to help transform raw data from the system into information that has the potential to have a positive impact on education [1]
Based on the description above, this study aims to examine the data held by the study program to predict students’ graduation as an evaluation material for S1 Industrial Engineering Study Program in IST AKPRIND

Summary

Introduction

Students at the tertiary level of education whose rights are regulated in Law no. 20 of 2003 Chapter V concerning Students. universities should have students’ data stored in information systems. The data consists of students’ registration data, students’ academic data for each semester to students’ graduation data After students graduate, these data tend not to be used optimally. These data tend not to be used optimally These data need to be utilized to obtain deep information. It is not easy to make predictions by utilizing various raw data held by the institute, so Educational Data Mining techniques are necessary to help transform raw data from the system into information that has the potential to have a positive impact on education [1]. Data Mining Education is a sub-area of Data Mining Domain. This new area has great potential to mine various aspects for improving the student’s quality as well as in decision making by educational institution authorities [2]. "Educational Data Mining is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from educational settings, and using those methods to understand the students better, and the settings which they learn in." [3]

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Conclusion