The Performance of Decision Tree and Ensemble Algorithms for Classifying the Graduation Status of Undergraduate Students at Universitas Negeri Jakarta

DOI

10.2991/978-94-6463-332-0_5How to use a DOI?

Keywords

Confusion Matrix; F1 Score; Machine Learning; Permutation Feature Importance

Abstract

A bachelor’s degree in Indonesia typically takes around four years to complete. This research aims to examine the data patterns related to the graduation status of a bachelor’s degree from Universitas Negeri Jakarta (UNJ). The data pattern is used to determine if an undergraduate student from UNJ will graduate on time or not on time. On time graduation occurs when the student takes four years or eight semesters to obtain their bachelor’s degree, while not on time graduation occurs if the student takes more than eight semesters. The graduation status is classified using AdaBoost and Random Forest algorithms, based on grade points and total credits earned from the student’s courses. The AdaBoost and Random Forest algorithms are contrasted with the simpler ML algorithm, Decision Tree. The study found that decision tree, AdaBoost, and Random Forest are effective in classifying an undergraduate student from UNJ based on their graduation status ‘on time’ or ‘not on time’ during a given semester (the first, the second, the third, the fourth, the fifth or the sixth semester). The student’s classification accuracy score reaches 64%. The Random Forest, AdaBoost, and Decision Tree all had accuracy scores of 64%, 63%, and 60%, respectively. Ensemble methods (AdaBoost and Random Forest) outperform the decision tree algorithm. For each semester, the mean difference in accuracy score between ensemble methods and decision tree for classifying a student’s graduation status reaches 2%-5%.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Dian Handayani
AU  - Abdurrahman Malik Karim
AU  - Dania Siregar
AU  - Faroh Ladayya
PY  - 2023
DA  - 2023/12/18
TI  - The Performance of Decision Tree and Ensemble Algorithms for Classifying the Graduation Status of Undergraduate Students at Universitas Negeri Jakarta
BT  - Proceedings of the 5th International Conference on Statistics, Mathematics, Teaching, and Research 2023 (ICSMTR 2023)
PB  - Atlantis Press
SP  - 33
EP  - 41
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-332-0_5
DO  - 10.2991/978-94-6463-332-0_5
ID  - Handayani2023
ER  -