Development of Model for Providing Feasible Scholarship
DOI:
https://doi.org/10.21512/commit.v10i1.1666Keywords:
Scholarship, Data Mining, Naive Bayes, Knowledge Discovery in DatabasesAbstract
The current work focuses on the development of a model to determine a feasible scholarship recipient on the basis of the naiv¨e Bayes’ method using very simple and limited attributes. Those attributes are the applicants academic year, represented by their semester, academic performance, represented by their GPa, socioeconomic ability, which represented the economic capability to attend a higher education institution, and their level of social involvement. To establish and evaluate the model performance, empirical data are collected, and the data of 100 students are divided into 80 student data for the model training and the remaining of 20 student data are for the model testing. The results suggest that the model is capable to provide recommendations for the potential scholarship recipient at the level of accuracy of 95%.
Plum Analytics
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