Open AccessPrediction of the graduation rate of engineering education students using Artificial Neural Network Algorithms
Author(s) -
Muhammad Anwar
Publication year - 2021
Publication title -
international journal of research in counseling and education
Language(s) - English
Resource type - Journals
eISSN - 2620-5769
pISSN - 2620-5750
DOI - 10.24036/00411za0002
Subject(s) - graduation (instrument) , particle swarm optimization , artificial neural network , computer science , selection (genetic algorithm) , machine learning , artificial intelligence , algorithm , engineering , mechanical engineering
The graduation rate of engineering education students on time dramatically affects the quality of learning. The purpose of this study is to predict the graduation rate of engineering education students. The method uses an artificial neural network algorithm combined with particle swarm optimization and forward selection, with 234 samples. The test results with Artificial Neural Network obtained 82.61% accuracy with predictions on time 149 and not on time 62. Artificial Neural Network with Particle Swarm Optimization obtained 91.30% accuracy with predictions on time 165, not on time 69. Furthermore, Artificial Neural Network with Particle Swarm Optimization and reduced by forwarding selection obtained 95.65% accuracy with predictions of the number of graduations on time 165 and not on time 69. Thus, the combination of the three algorithms can predict the graduation rate of engineering education students with high accuracy.