Prediction of Teachers' Lateness Factors Coming to School Using C4.5, Random Tree, Random Forest Algorithm

DOI

10.2991/icream-18.2019.34How to use a DOI?

Keywords

data mining; C4.5; random tree; random forest; accuracy; AUC

Abstract

Lateness arrives at work can be experienced by anyone, including teachers. Teachers who are late arriving at school have shown examples of bad behavior for students. It takes a study to determine the factors that cause a teacher to arrive late to school. Data Mining is selected to process the data that has been available. Processing uses 3 classification algorithms which are decision tree (C4.5, Random Tree, and Random Forest) algorithms. All three algorithms will be tested for known performance, where the best algorithm is determined by accuracy and AUC. The results of the research were obtained that Random Forest with pruning and pre-pruning is the best for accuracy value with 74.63% and also AUC value with 0.743. The teacher's delay in this study is often done by teachers who have a vehicle compared to those who do not have a vehicle.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Windu Gata
AU  - Grand Grand
AU  - Rhini Fatmasari
AU  - Baharuddin Baharuddin
AU  - Yuyun Elizabeth Patras
AU  - Rais Hidayat
AU  - Siswanto Tohari
AU  - Nia Kusuma Wardhani
PY  - 2019/03
DA  - 2019/03
TI  - Prediction of Teachers' Lateness Factors Coming to School Using C4.5, Random Tree, Random Forest Algorithm
BT  - Proceedings of the 2nd International Conference on Research of Educational Administration and Management (ICREAM 2018)
PB  - Atlantis Press
SP  - 161
EP  - 166
SN  - 2352-5398
UR  - https://doi.org/10.2991/icream-18.2019.34
DO  - 10.2991/icream-18.2019.34
ID  - Gata2019/03
ER  -