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This is a Quantitative Analysis study.

September 5, 2025Cerdika Jurnal Ilmiah Indonesia

Machine Learning-Based Classification For Selection Of Smart Scholarship Fee Amounts In The Capital City

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Decision Tree model achieved an accuracy of 88.31%, outpacing Naïve Bayes and Random Forest for KJP scholarship management.
Using machine learning algorithms improved decision-making for KJP scholarship fund allocations in Jakarta.
Data was processed using the SEMMA approach and involved historical scholarship recipient documentation.
This integration of machine learning supports efficiency and precision in the management of educational funds.

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Implication

Quantitative analysis found that machine learning models enhance accuracy in KJP scholarship distribution, indicating benefits for educational funding management.

Cite This Study

Wiza Teguh (2025) studied this question.

synapsesocial.com/papers/68bb420d2b87ece8dc957fda https://doi.org/10.59141/cerdika.v5i2.2497

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Implication

Quantitative analysis found that machine learning models enhance accuracy in KJP scholarship distribution, indicating benefits for educational funding management.

Machine Learning-Based Classification For Selection Of Smart Scholarship Fee Amounts In The Capital City

Key Points

Discussion

Implication

Cite This Study

Machine Learning-Based Classification For Selection Of Smart Scholarship Fee Amounts In The Capital City

Key Points

Discussion

Implication

Cite This Study

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