IMPLEMENTATION OF PARTICLE SWARM OPTIMIZATION BASED MACHINE LEARNING ALGORITHM FOR STUDENT PERFORMANCE PREDICTION

  • Muhammad Iqbal (1*) STMIK Nusa Mandiri
  • Irwan Herliawan (2) STMIK Nusa Mandiri
  • Ridwansyah Ridwansyah (3) STMIK Nusa Mandiri
  • Windu Gata (4) STMIK Nusa Mandiri
  • Abdul Hamid (5) STMIK Nusa Mandiri
  • Jajang Jaya Purnama (6) STMIK Nusa Mandiri
  • Yudhistira Yudhistira (7) STMIK Nusa Mandiri

  • (*) Corresponding Author
Keywords: Student Performance, Machine Learning, Particle Swarm Optimization, Prediction

Abstract

Education plays an important role in the development of a country, especially educational institutions as places where the educational process has an important goal to create quality education in improving student performance. Based on research conducted in the last few decades the quality of education in Portugal has improved, but statistics show that the failure rate of students in Portugal is high, especially in the fields of Mathematics and Portuguese. On the other hand, machine learning which is part of Artificial Intelligence is considered to be helpful in the field of education, one of which is in predicting student performance. However, measuring student performance becomes a challenge since student performance has several factors, one of which is the relationship of variables and factors for predicting the performance of participating in an orderly manner. This study aims to find out how the application of machine learning algorithms based on particle sworm optimization to predict student performance. By using experimental research methods and the results of empirical studies shown in each model, namely random forest, decision tree, support vector machine and particle swarm optimization based neural network can improve the accuracy of student performance predictions.

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Published
2021-02-02
How to Cite
[1]
M. Iqbal, “IMPLEMENTATION OF PARTICLE SWARM OPTIMIZATION BASED MACHINE LEARNING ALGORITHM FOR STUDENT PERFORMANCE PREDICTION”, jitk, vol. 6, no. 2, pp. 195-204, Feb. 2021.
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