Automatic Essay Grading for Bahasa Indonesia with Support Vector Machine and Latent Semantic Analysis

Anak Agung Putri Ratna, Hanifah Khairunissa, Aaliyah Kaltsum, Ihsan Ibrahim, Prima Dewi Purnamasari

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This research is used to increase the accuracy of automatic short essay grading for Bahasa Indonesia. Short essay in Bahasa Indonesia are classified using Support Vector Machine (SVM) based on its topic to decrease unrelated answer then assessed using Latent Semantic Analysis. Term Frequency-Inverse Document Frequency (TF-IDF) is used to weigh word in short essay and the result will be an input on SVM. The output of SVM are assessed using Latent Semantic Analysis (LSA). Latent Semantic Analysis uses Term Frequency Matrix to represent text in matrix, Singular Value Decomposition to decompose these matrix, and Frobenius Norm to find the similarity of lectures' answer and students' answer In this research, parameter C value as 1 and kernel linear are used to obtain the highest accuracy of classification using Support Vector Machine, 97,297% with 50% portion of data as training and 50% portion of data as testing. The accuracy score obtained from LSA is 72,01%.

Original languageEnglish
Title of host publicationICECOS 2019 - 3rd International Conference on Electrical Engineering and Computer Science, Proceeding
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages363-367
Number of pages5
ISBN (Electronic)9781728147147
DOIs
Publication statusPublished - Oct 2019
Event3rd International Conference on Electrical Engineering and Computer Science, ICECOS 2019 - Batam, Indonesia
Duration: 2 Oct 20193 Oct 2019

Publication series

NameICECOS 2019 - 3rd International Conference on Electrical Engineering and Computer Science, Proceeding

Conference

Conference3rd International Conference on Electrical Engineering and Computer Science, ICECOS 2019
CountryIndonesia
CityBatam
Period2/10/193/10/19

Keywords

  • e-learning
  • essay grading
  • Japanese language
  • latent semantic analysis
  • support vector machine
  • term frequency-inverse document frequency

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  • Cite this

    Putri Ratna, A. A., Khairunissa, H., Kaltsum, A., Ibrahim, I., & Purnamasari, P. D. (2019). Automatic Essay Grading for Bahasa Indonesia with Support Vector Machine and Latent Semantic Analysis. In ICECOS 2019 - 3rd International Conference on Electrical Engineering and Computer Science, Proceeding (pp. 363-367). [8984528] (ICECOS 2019 - 3rd International Conference on Electrical Engineering and Computer Science, Proceeding). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICECOS47637.2019.8984528