Automatic Essay Grading System for Japanese Language Exam using CNN-LSTM

Amanda Nur Oktaviani, Marwah Zulfanny Alief, Lea Santiar, Prima Dewi Purnamasari, Anak Agung Putri Ratna

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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%.

Original languageEnglish
Title of host publication17th International Conference on Quality in Research, QIR 2021
Subtitle of host publicationInternational Symposium on Electrical and Computer Engineering
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages164-169
Number of pages6
ISBN (Electronic)9781665496964
DOIs
Publication statusPublished - 2021
Event17th International Conference on Quality in Research, QIR 2021: International Symposium on Electrical and Computer Engineering - Virtual, Online, Indonesia
Duration: 13 Oct 202115 Oct 2021

Publication series

Name17th International Conference on Quality in Research, QIR 2021: International Symposium on Electrical and Computer Engineering

Conference

Conference17th International Conference on Quality in Research, QIR 2021: International Symposium on Electrical and Computer Engineering
Country/TerritoryIndonesia
CityVirtual, Online
Period13/10/2115/10/21

Keywords

  • Automatic Essay Grading System
  • Convolutional Neural Network
  • Long Short-term Memory
  • Natural Language Processing

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