Predicting student placement class using data mining

Author

Pratiwi, Oktariani Nurul

Author_Institution

Tek. Inf., Univ. Widyatama, Bandung, Indonesia

fYear

2013

fDate

26-29 Aug. 2013

Firstpage

618

Lastpage

621

Abstract

All students in first grade of senior high school in Indonesia have to pass the step called placement class. It divide into Science, Social or Literature class. In traditional method, the placement class process conducted by teachers. But, it needed much time to decide the right class for students. The proposed is using the Knowledge Discovery and Data Mining (KDD). Which is the placement class process using the classification method. In the first experiment classified instances 84.2%. The second experiment use the same data and attributes, give the best percentage of accuracy as 92,1%. The best result are using Naive Bayes and SMO. Hope in the future, it can be the solution to help teacher decide the placement class.

Keywords

Bayes methods; data analysis; data mining; educational administrative data processing; further education; pattern classification; statistical analysis; Indonesia; KDD; Literature class; SMO; Science class; Social class; classification method; data analysis; knowledge discovery-and-data mining; naive Bayes; senior high school; statistical techniques; student placement class prediction; Accuracy; Algorithm design and analysis; Classification algorithms; Data mining; Educational institutions; Prediction algorithms; classification; data mining; education; placement class;

fLanguage

English

Publisher

ieee

Conference_Titel

Teaching, Assessment and Learning for Engineering (TALE), 2013 IEEE International Conference on

Conference_Location

Bali

Type

conf

DOI

10.1109/TALE.2013.6654511

Filename

6654511