TY - GEN
T1 - Automatic Essay Grading System for Japanese Language Exam using CNN-LSTM
AU - Oktaviani, Amanda Nur
AU - Alief, Marwah Zulfanny
AU - Santiar, Lea
AU - Purnamasari, Prima Dewi
AU - Ratna, Anak Agung Putri
N1 - Funding Information:
ACKNOWLEDGMENT The authors would like to thank 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:
©2021 IEEE
PY - 2021
Y1 - 2021
N2 - This paper discusses the design for the development of an automatic essay grading system (SIMPLE-O) using variations of the Convolutional Neural Network (CNN) and hybrid Convolutional Neural Network (CNN)-Long Short-term Memory (LSTM) for the assessment of the Japanese essay exam which is being developed by the Department of Electrical Engineering, University of Indonesia. Of the several variations tested, the most stable model is a model that has CNN-LSTM with kernel sizes of 5, the number of filters 64, pool size of 4, LSTM hidden units of 25, batch size of 50, repeated training of 50 epochs, and the SGD optimizer with a learning rate of 0.01 produces the highest prediction accuracy, which is 70.07%.
AB - This paper discusses the design for the development of an automatic essay grading system (SIMPLE-O) using variations of the Convolutional Neural Network (CNN) and hybrid Convolutional Neural Network (CNN)-Long Short-term Memory (LSTM) for the assessment of the Japanese essay exam which is being developed by the Department of Electrical Engineering, University of Indonesia. Of the several variations tested, the most stable model is a model that has CNN-LSTM with kernel sizes of 5, the number of filters 64, pool size of 4, LSTM hidden units of 25, batch size of 50, repeated training of 50 epochs, and the SGD optimizer with a learning rate of 0.01 produces the highest prediction accuracy, which is 70.07%.
KW - Automatic Essay Grading System
KW - Convolutional Neural Network
KW - Long Short-term Memory
KW - Natural Language Processing
UR - http://www.scopus.com/inward/record.url?scp=85127000551&partnerID=8YFLogxK
U2 - 10.1109/QIR54354.2021.9716165
DO - 10.1109/QIR54354.2021.9716165
M3 - Conference contribution
AN - SCOPUS:85127000551
T3 - 17th International Conference on Quality in Research, QIR 2021: International Symposium on Electrical and Computer Engineering
SP - 164
EP - 169
BT - 17th International Conference on Quality in Research, QIR 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 17th International Conference on Quality in Research, QIR 2021: International Symposium on Electrical and Computer Engineering
Y2 - 13 October 2021 through 15 October 2021
ER -