Ermatita, Ermatita (2022) Similarity Filter-Based Feature Selection Method for Predicting Students Academic Performance. Turnitin Universitas Sriwijaya. (Submitted)
![[thumbnail of Similarity Filter-Based Feature Selection Method for Predicting Students Academic Performance.pdf]](http://repository.unsri.ac.id/style/images/fileicons/text.png) |
Text
Similarity Filter-Based Feature Selection Method for Predicting Students Academic Performance.pdf Download (1MB) |
Abstract
Generally, almost all higher education often face the same problem of improving their quality according to students' academic performance. The need to get early information about the poor students' academic performance has forced higher education to find the best solution that the prediction model could achieve. Data mining offers various algorithms for predicting. Therefore, constructing an accurate prediction model becomes a challenging task for higher education. Two factors that drive the accuracy of the prediction model are classifiers and feature selection. Each classifier gives the best result if it meets the appropriate categorized data on a dataset. A few research has provided excellent results in predicting students' academic performance. But, the research only focuses on the classification technique rather than the right feature selection. Vice versa, a few research have reported excellent results increasing the prediction model accuracy. But the research only focuses on feature selection techniques rather than carrying out the right classifier on the right data. Therefore, the prediction model has not given the best accuracy yet. Unlike than existing framework to build a model and select the features ignoring the categorized data on a dataset, this research proposes the right filter-based feature selection methods and the right classifiers based on categorized data. The result will help the researcher find the best combination of filter-based feature selection methods and classifiers. Various classification algorithms and various feature selections that have been tested show classification with appropriate classifiers for specific categorized data and proper feature selection increase the prediction model's accuracy.
| Item Type: | Other |
|---|---|
| Subjects: | #3 Repository of Lecturer Academic Credit Systems (TPAK) > Results of Ithenticate Plagiarism and Similarity Checker |
| Divisions: | 09-Faculty of Computer Science > 55101-Informatics (S2) |
| Depositing User: | Dr Ermatita zuhairi |
| Date Deposited: | 25 Jun 2024 06:10 |
| Last Modified: | 25 Jun 2024 06:10 |
| URI: | http://repository.unsri.ac.id/id/eprint/147767 |
Actions (login required)
 |
View Item |