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The Decision Tree C5.0 Classification Algorithm for Predicting Student Academic Performance

Benediktus, Natanael and Oetama, Raymond Sunardi (2020) The Decision Tree C5.0 Classification Algorithm for Predicting Student Academic Performance. Ultimatics : Jurnal Teknik Informatika, 12 (1). pp. 14-19. ISSN 2085-4552

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Official URL: https://ejournals.umn.ac.id/index.php/TI/article/v...

Abstract

Student’s performance is often used as a benchmark and a student’s activeness is frequently used as a criteria of how well a student academically perform at school. Where in this study would try to find out whether the activeness of a student can predict their academic performance. The data used is an educational dataset is collected using a learning management system (LMS), which is a learner activity tracker tool that is connected by the internet. This data has numerical and categorical variables, so it is needed to have the right algorithm to classify data accurately and ensure data validity. In this study, the C.50 algorithm is used to test the data, where the data is divided into training data by 75% and testing data by 25%. And the result from the tested data, an accuracy of 71.667% is obtained.

Item Type: Article
Subjects: 000 Computer Science, Information and General Works > 000 Computer Science, Knowledge and Systems > 006 Special Computer Methods (Artificial Intelligence, Machine Learning, 3D Graphics, Digital Video, Data Mining, Augmented Reality)
600 Technology (Applied Sciences) > 600 Technology > 607 Education, Research, Related Topics
Q Science > QA Mathematics > QA273-280 Probabilities. Mathematical statistics
Divisions: Fakultas Teknik Informatika > Program Studi Sistem Informasi
Depositing User: mr admin umn
Date Deposited: 25 Nov 2021 09:00
Last Modified: 25 Nov 2021 09:00
URI: http://kc.umn.ac.id/id/eprint/19290

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