An Internet-based Student Admission Screening System utilizing Data Mining

This study aimed to propose an internet-based student admission screening system utilizing data mining in order for officers to reduce time to evaluate applicants as well as for the faculty to use less human resources on screening applicants that meets their proficiency and criteria of each department. Another benefit is that the system can help applicants efficiently choose a specialization that is suitable to their proficiency and capability. The system used a decision tree based classification method. Prior to system development, six models were created and tested to find the most efficient model which would later be applied for development of internet-based student admission screening system. The first three of six models employed a k-fold cross validation technique, while the remaining three models use a percentage split test technique. Experiment results revealed that the most efficient model was the data classification model that uses Percentage Split (80), which provided the precision of 87.90%, recall of 87.80%, F-measure of 87.60% and accuracy of 87.82%. To make the efficient student admission screening system, this experiment selected a data classification model that implements Percentage Split (80).

@article{Phongphanich2017,
  title     = {An Internet-based Student Admission Screening System utilizing Data Mining},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {8},
  number    = {6},
  year      = {2017},
  publisher = {The Science and Information Organization},
  author    = {Dolluck Phongphanich and Wirat Choonui},
  doi       = {10.14569/IJACSA.2017.080626},
  url       = {https://doi.org/10.14569/IJACSA.2017.080626}
}