TY - GEN
T1 - Automatic essay grading system based on latent semantic analysis with learning vector quantization and word similarity enhancement
AU - Ratna, Anak Agung Putri
AU - Arbani, Adam Arsy
AU - Ibrahim, Ihsan
AU - Ekadiyanto, F. Astha
AU - Bangun, Kristofer Jehezkiel
AU - Purnamasari, Prima Dewi
N1 - Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018/11/23
Y1 - 2018/11/23
N2 - Department of Electrical Engineering Universitas Indonesia has developed an automatic essay grading system called Simple-O since 2007. Simple-O uses the Latent Semantic Analysis (LSA) method to compare two essays by extracting the essay into matrix. The previous development of Simple-O is the addition of Learning Vector Quantization (LVQ) which is a method of artificial neural network. This research will discuss and provide analysis related to the effect of adding word similarity function to the automatic essay grading system (Simple-O) to the accuracy of the system itself. The experiment will be conducted with five different scenarios by varying the number of keywords in the student’s answer essay to 100%, 80%, 60%, 40%, and 20% of the reference essay keywords. According to the result, there are scenarios that has decreased and increased in accuracy. The average accuracy of the Simple-O system after the addition of word similarity function has increased, though not significant. The average increase in accuracy after the addition of word similarity function is 5.4% from 90.9% to 96.3%.
AB - Department of Electrical Engineering Universitas Indonesia has developed an automatic essay grading system called Simple-O since 2007. Simple-O uses the Latent Semantic Analysis (LSA) method to compare two essays by extracting the essay into matrix. The previous development of Simple-O is the addition of Learning Vector Quantization (LVQ) which is a method of artificial neural network. This research will discuss and provide analysis related to the effect of adding word similarity function to the automatic essay grading system (Simple-O) to the accuracy of the system itself. The experiment will be conducted with five different scenarios by varying the number of keywords in the student’s answer essay to 100%, 80%, 60%, 40%, and 20% of the reference essay keywords. According to the result, there are scenarios that has decreased and increased in accuracy. The average accuracy of the Simple-O system after the addition of word similarity function has increased, though not significant. The average increase in accuracy after the addition of word similarity function is 5.4% from 90.9% to 96.3%.
KW - Essay grading
KW - Latent semantic analysis
KW - Learning vector quantization
KW - Singular value decomposition
KW - Word similarity
UR - http://www.scopus.com/inward/record.url?scp=85062800418&partnerID=8YFLogxK
U2 - 10.1145/3293663.3293684
DO - 10.1145/3293663.3293684
M3 - Conference contribution
AN - SCOPUS:85062800418
T3 - ACM International Conference Proceeding Series
SP - 120
EP - 126
BT - AIVR 2018 - 2018 International Conference on Artificial Intelligence and Virtual Reality
PB - Association for Computing Machinery
T2 - 2018 International Conference on Artificial Intelligence and Virtual Reality, AIVR 2018
Y2 - 23 November 2018 through 25 November 2018
ER -