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

被引：0

作者：

Gata, Windu ^{[1
]}

Grand, Grand ^{[1
]}

Patras, Yuyun Elizabeth ^{[2
]}

Hidayat, Rais ^{[2
]}

Fatmasari, Rhini ^{[3
]}

Tohari, Siswanto ^{[4
]}

Baharuddin, Baharuddin ^{[5
]}

Wardhani, Nia Kusuma ^{[6
]}

机构：

[1] Post Grad STMIK Nusa Mandiri, Comp Sci, Jakarta, Indonesia

[2] Pakuan Univ, Educ Management, Bogor, Indonesia

[3] Univ Terbuka, Educ Management, Jakarta, Indonesia

[4] Budi Luhur Univ, Fac IT, Jakarta, Indonesia

[5] Univ Muhammadiyah Makassar, Fac Islamic Relig, Makassar, Indonesia

[6] Mercubuana Univ, Fac Econ, Jakarta, Indonesia

来源：

PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON RESEARCH OF EDUCATIONAL ADMINISTRATION AND MANAGEMENT (ICREAM 2018) | 2018年 / 258卷

关键词：

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

D O I：

暂无

中图分类号：

G40 [教育学];

学科分类号：

040101 ; 120403 ;

摘要：

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.

引用

页码：161 / 166

页数：6

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