A Review on Data Mining and Machine Learning Methods for Student Scholarship Prediction

Hegde, Ramakrishna; Anusha, G.; Madival, Sindhu; Sowjanya, H S; Sushma, U

doi:10.1109/iccmc51019.2021.9418376

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…These scholarships can come from government, private or foundation institutions [4]. Scholarship providers generally provide some criteria that must be met, for example, parents' income, number of dependents, GPA, achievement of scholarship recipients, living costs, and others [3].…”

Section: Introductionmentioning

confidence: 99%

Determination of Bank Indonesia Scholarship Recipients Using Naïve Bayes Classifier

Febri,

Sari

2023

BAREKENG: J. Math. & App.

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A scholarship is a grant given to students as financial aid for education. One of the most sought-after scholarships is the scholarship from Bank Indonesia. Currently, the selection process for Bank Indonesia scholarship recipients still involves verifying the completeness of the administrative documents of the prospective recipients. Manual administrative verification requires a long time for data processing and re-verification. Therefore, there is a need for a data classification system to assist in the decision-making process for Bank Indonesia scholarship recipients. This study aims to implement the naïve Bayes classifier method to classify Bank Indonesia scholarships accurately. The variables used include gender, semester, parental income, grade point average (GPA), achievement, organizational activity, and the number of dependents. This research found that the naïve Bayes classifier method for classifying Bank Indonesia scholarship recipients can be done accurately with an accuracy rate of 86,84%.

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Section: Introductionmentioning

confidence: 99%