TY - GEN
T1 - Automatic Essay Grading for Bahasa Indonesia with Support Vector Machine and Latent Semantic Analysis
AU - Putri Ratna, Anak Agung
AU - Khairunissa, Hanifah
AU - Kaltsum, Aaliyah
AU - Ibrahim, Ihsan
AU - Purnamasari, Prima Dewi
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - 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%.
AB - 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%.
KW - e-learning
KW - essay grading
KW - Japanese language
KW - latent semantic analysis
KW - support vector machine
KW - term frequency-inverse document frequency
UR - http://www.scopus.com/inward/record.url?scp=85086010186&partnerID=8YFLogxK
U2 - 10.1109/ICECOS47637.2019.8984528
DO - 10.1109/ICECOS47637.2019.8984528
M3 - Conference contribution
AN - SCOPUS:85086010186
T3 - ICECOS 2019 - 3rd International Conference on Electrical Engineering and Computer Science, Proceeding
SP - 363
EP - 367
BT - ICECOS 2019 - 3rd International Conference on Electrical Engineering and Computer Science, Proceeding
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Electrical Engineering and Computer Science, ICECOS 2019
Y2 - 2 October 2019 through 3 October 2019
ER -