@inproceedings{0966d7b229d3497db0eabaca0a0fe9a9,
title = "Comparison of MLP-BPNN and MLP-PSO for Automatic Essay Grading System for Japanese Language Exam",
abstract = "In this paper, a study was conducted for a hybrid model for Multilayer Perceptron (MLP) with Particle Swarm Optimization (PSO). The PSO was used to replace the Backpropagation method for the weight optimization. The comparison was conducted between MLP-BPNN and MLP-PSO for an automated essay grading system for Japanese language exam. The MLP-PSO model achieved a more accurate but less stable result. The MLP-PSO model with 10 particles trained for 15 steps achieves the best result out of the two MLP-PSO models tested, with an average 8.48% error for the grade population. Compared to the MLP-PSO model, it was discovered that MLP-BPNN with Adam optimizer achieves better overall performance and results concerning both the accuracy and the stability of the model.",
keywords = "Machine Learning, Multilayer, Neural Network, Particle Swarm Optimization, Perceptron, SIMPLE-O",
author = "Putra, {Farhan P.} and Purnamasari, {Prima Dewi} and Ratna, {Anak Agung Putri} and Lea Santiar",
note = "Funding Information: The authors would like to thank the Directorate of Higher Education Ministry of Education Indonesia for supporting this research under the PTUPT 2021 grant no NKB-274/UN2.RST/HKP.05.00/2021. Publisher Copyright: {\textcopyright}2021 IEEE; 17th International Conference on Quality in Research, QIR 2021: International Symposium on Electrical and Computer Engineering ; Conference date: 13-10-2021 Through 15-10-2021",
year = "2021",
doi = "10.1109/QIR54354.2021.9716163",
language = "English",
series = "17th International Conference on Quality in Research, QIR 2021: International Symposium on Electrical and Computer Engineering",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "204--208",
booktitle = "17th International Conference on Quality in Research, QIR 2021",
address = "United States",
}