PEMODELAN PREDIKSI KINERJA SISWA DENGAN HYBRID PARTICLE SWARM OPTIMIZATION-SUPPORT VECTOR REGRESSION (PSO-SVR) DAN GENETIC ALGORITHM-SUPPORT VECTOR REGRESSION (GA-SVR)

APRIYADI, M. RIKI and Ermatita, Ermatita and Rini, Dian Palupi (2023) PEMODELAN PREDIKSI KINERJA SISWA DENGAN HYBRID PARTICLE SWARM OPTIMIZATION-SUPPORT VECTOR REGRESSION (PSO-SVR) DAN GENETIC ALGORITHM-SUPPORT VECTOR REGRESSION (GA-SVR). Doctoral thesis, Sriwijaya University.

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Abstract

Improving the quality of education is very important in the development of a country. Identifying representative educational data and conducting predictive analysis of student performance in an educational institution is very important to assist management in determining strategies and making decisions to improve student performance. This study modeled student performance using Hybrid Particle Swarm Optimization-Support Vector Regression (PSO-SVR) and Genetic Algorithm-Support Vector Regression (GA-SVR). This study uses predictive modeling of student performance with SVR modeling. PSO and GA are used as feature selections to identify relevant factors that influence student performance. The feature selection results, which are selected features, are then predicted with SVR to produce FSPSO-SVR and FSGA-SVR modeling. This study also performs hyperparameter optimization on SVR to obtain the best hyperparameters using PSO and GA, resulting in PSO-SVR (PSVR) and GA-SVR (GSVR) modeling. In the final stage, a comparison of the results of regression performance metrics was carried out from this study and previous studies. From the results of this study, the SVR modeling produced an RMSE value of 4.519; the FSPSO-SVR modeling produced an RMSE value of 2.954; the FSGA-SVR modeling produced an RMSE value of 4.447; the PSVR modeling resulted in an RMSE value of 1.608; the GSVR modeling had an RMSE value of 1.830. Meanwhile, from previous research conducted by Cortez on SVR modeling, the RMSE value was 2.090. The comparison of regression performance metrics found that the best student performance prediction modeling used PSVR modeling with a decrease in error values of 64.42% compared to SVR modeling in this study and a decrease in error values of 23.06% compared to previous studies.

Item Type: Thesis (Doctoral)
Uncontrolled Keywords: Kinerja Siswa, Prediksi, Seleksi Fitur, Hyperparamater, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Support Vector Regression (SVR).
Subjects: Q Science > Q Science (General) > Q300-390 Cybernetics > Q325.5 Machine learning
Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions: 03-Faculty of Engineering > 21001-Engineering Science (S3)
Depositing User: M. Riki Apriyadi
Date Deposited: 10 May 2023 02:08
Last Modified: 10 May 2023 02:08
URI: http://repository.unsri.ac.id/id/eprint/101228

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