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

  • Agus Hindarto Wibowo Institut Sains dan Teknologi AKPRIND

Abstract

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.

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Published
Mar 30, 2020
How to Cite
WIBOWO, Agus Hindarto. Graduation Prediction of S1 Industrial Engineering Students IST AKPRIND by Using Data Mining Method. Logic : Jurnal Rancang Bangun dan Teknologi, [S.l.], v. 20, n. 1, p. 19-24, mar. 2020. ISSN 2580-5649. Available at: <http://ojs.pnb.ac.id/index.php/LOGIC/article/view/1580>. Date accessed: 04 june 2020. doi: http://dx.doi.org/10.31940/logic.v20i1.1580.
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Articles