TY - GEN
T1 - Automatic essay grading system with latent semantic analysis and learning vector quantization
AU - Ratna, Anak Agung Putri
AU - Raharjo, Budi Selamet
AU - Purnamasari, Prima Dewi
AU - Sanjaya, Randy
N1 - Publisher Copyright:
© 2017 Association for Computing Machinery.
PY - 2017/11/24
Y1 - 2017/11/24
N2 - Automatic essay grading system called SIMPLE-O (Sistem Penilai Esai Otomatis) that developed by Department of Electrical Engineering, Faculty of Engineering, Universitas Indonesia was built using PHP. This system was developed for helping lecturer assessing the examination, especially with essay form. Currently, the SIMPLE-O is still developed using C programming language for implementing more methods in development that can only be done in that language to improve the performance of the system. To increase the accuracy, Learning Vector Quantization (LVQ) algorithm is implemented in the development due to its ability for supervised classification. The number of data samples in LVQ training phase are affecting the essay scoring performance, less data used will lead to decrease in the result accuracy of the validation phase. Moreover, singular value that generated by Frobenius norm and vector angle pre-processing will also affect the scoring accuracy. But, the number of words-per-column when creating the LSA matrix did not have any significant effect. At the end, SIMPLE-O with LVQ has an average accuracy of 53.57%, 41.66% higher than the system that did not use LVQ. This accuracy performance was still low due to the missing of the words similarity feature. In the previous version of SIMPLE-O, this feature can improve the performance of the essay grading system significantly.
AB - Automatic essay grading system called SIMPLE-O (Sistem Penilai Esai Otomatis) that developed by Department of Electrical Engineering, Faculty of Engineering, Universitas Indonesia was built using PHP. This system was developed for helping lecturer assessing the examination, especially with essay form. Currently, the SIMPLE-O is still developed using C programming language for implementing more methods in development that can only be done in that language to improve the performance of the system. To increase the accuracy, Learning Vector Quantization (LVQ) algorithm is implemented in the development due to its ability for supervised classification. The number of data samples in LVQ training phase are affecting the essay scoring performance, less data used will lead to decrease in the result accuracy of the validation phase. Moreover, singular value that generated by Frobenius norm and vector angle pre-processing will also affect the scoring accuracy. But, the number of words-per-column when creating the LSA matrix did not have any significant effect. At the end, SIMPLE-O with LVQ has an average accuracy of 53.57%, 41.66% higher than the system that did not use LVQ. This accuracy performance was still low due to the missing of the words similarity feature. In the previous version of SIMPLE-O, this feature can improve the performance of the essay grading system significantly.
KW - Essay grading
KW - Latent semantic analysis
KW - Learning vector quantization
KW - SIMPLE-O
KW - Singular value decomposition
UR - http://www.scopus.com/inward/record.url?scp=85042085535&partnerID=8YFLogxK
U2 - 10.1145/3162957.3162989
DO - 10.1145/3162957.3162989
M3 - Conference contribution
AN - SCOPUS:85042085535
T3 - ACM International Conference Proceeding Series
SP - 158
EP - 163
BT - Proceedings of the 3rd International Conference on Communication and Information Processing, ICCIP 2017
PB - Association for Computing Machinery
T2 - 3rd International Conference on Communication and Information Processing, ICCIP 2017
Y2 - 24 November 2017 through 26 November 2017
ER -