Random forest algorithm for algorithm for prediction of high school science students acceptance snmptn based on students assesment report
Abstract
National Selection for State University (SNMPTN) is one of the selection
lines for admission of new students in Indonesia to enter State Universities by
invitation. Report card grades are one component of the assessment of
admission of new students to enter state universities on this pathway. The
difference in standards between universities in determining the admission of
SNMPTN applicants, causing the need to predict based on several related
factors. This research uses data mining techniques with Random forest
algorithm. From the results of research that has been done, it was found that
the Random Forest algorithm can be used to predict students who are accepted
at SNMPTN based on report card grades, obtained from the results of the
classification process with the student report card report survey dataset
received by SNMPTN, This is indicated by the accuracy, precision, and recall
values of 93%. Optimization of the random forest algorithm using the
oversampling technique with the SMOTE method can improve the classifier's
performance due to the imbalanced class problem.
Downloads
Published
Issue
Section
License
Authors who publish with Jurnal Informatika (JIFO) agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
