Attribute selection on student performance dataset using maximum dependency attribute

As a higher education institution, knowing which GPA of the semester has the most determinant to affecting the academic performance of students is important yet challenging. Therefore, this paper deliberates the usage of rough set theory based Maximum Dependency Attributes (MDA). The dataset is taken from the Directorate of Information Systems (SISFO), Telkom University. The result showed that the most determinant attribute is 2th GPA, followed with 3rd GPA, Entrance Examination, 1st GPA, Academic Aptitude Test, and 4th GPA, respectively. By early knowing the most determinant attribute in which score of the m, a well-planned strategic program can be set during the institution academic study period.

[1]  Surjeet Kumar Yadav,et al.  Mining Education Data to Predict Student's Retention: A comparative Study , 2012, ArXiv.

[2]  Mustafa Mat Deris,et al.  Applying variable precision rough set model for clustering student suffering study's anxiety , 2012, Expert Syst. Appl..

[3]  Carlos Márquez-Vera,et al.  Predicting student failure at school using genetic programming and different data mining approaches with high dimensional and imbalanced data , 2013, Applied Intelligence.

[4]  Theresa Beaubouef,et al.  Rough Sets , 2019, Lecture Notes in Computer Science.

[5]  Iwan Tri Riyadi Yanto,et al.  An Application of Rough Set Theory for Clustering Performance Expectancy of Indonesian e-Government Dataset , 2016, SCDM.

[6]  Jemal H. Abawajy,et al.  A rough set approach for selecting clustering attribute , 2010, Knowl. Based Syst..

[7]  Tutut Herawan,et al.  Mining Significant Association Rules from on Information and System Quality of Indonesian E-Government Dataset , 2016, SCDM.

[8]  Suhirman,et al.  A soft set approach for clustering student assessment datasets , 2015 .

[9]  Tutut Herawan,et al.  An Efficient Soft Set-Based Approach for Conflict Analysis , 2016, PloS one.

[10]  Osmar R. Zaïane,et al.  Application of Data Mining Techniques for Medical Image Classification , 2001, MDM/KDD.

[11]  Azuraliza Abu Bakar,et al.  Comparative Analysis of Data Mining Techniques for Malaysian Rainfall Prediction , 2016 .

[12]  Hairulnizam Mahdin,et al.  Soft Set Approach for Clustering Graduated Dataset , 2016, SCDM.

[13]  Haruna Chiroma,et al.  A Framework for Clustering of Web Users Transaction Based on Soft Set Theory , 2015, DaEng.