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All Journal Journal Of Artificial Intelligence And Software Engineering
Al Khaidar
Politeknik Negeri Lhokseumawe
Computer Science & IT

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Application of the Random Forest Method for UKT Classification at Politeknik Negeri Lhokseumawe Al Khaidar; Muhammad Arhami; Mustainul Abdi
Journal of Artificial Intelligence and Software Engineering Vol 4, No 2 (2024)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v4i2.6131

Abstract

Classification is the systematic grouping of objects, ideas, books, or other items into specific classes based on similar characteristics. One of its applications is in the grouping of tuition fees, which are fees paid each semester or academic year based on the student's economic ability. However, there are several issues, such as the placement of underprivileged students into fee groups that are still not appropriate and the limited accuracy of the grouping process due to it being done manually. To address these issues, a classification system was designed using the Random Forest method. Random Forest is a machine learning algorithm that combines multiple decision trees for more accurate predictions. Testing the Random Forest method using cross-validation shows an average accuracy of 95%. Evaluation with a confusion matrix shows an accuracy of 94%, with varying values of precision, recall, and f1-score for each group.
Co-Authors Arhami, Muhammad Mustainul Abdi