TY - GEN
T1 - K-means clustering for answer categorization on latent semantic analysis automatic Japanese short essay grading system
AU - Ratna, Anak Agung Putri
AU - Noviaindriani, Rashelia Radela
AU - Santiar, Lea
AU - Ibrahim, Ihsan
AU - Purnamasari, Prima Dewi
PY - 2019/7
Y1 - 2019/7
N2 - This paper discusses about the development of an automatic essay grading system for Japanese short essay answer by applying the K-Means Clustering to group each question's topic and Latent Semantic Analysis to make the assessment. The system is developed to help facilitate the examination of essay answers that are currently still being done manually. The development of the system itself is done by using Python programming language. The test scenarios were carried out by varying the types of hiragana and romaji input also the process of stopwords elimination. From the results obtained and the analysis carried out, the form or type of text input used and the use of parameter such as stopwords affect the accuracy of the assessment. The developed automatic essay grading system was able to obtain the highest accuracy rate of 89% by using input in the form of romaji letters and without the stopwords elimination process.
AB - This paper discusses about the development of an automatic essay grading system for Japanese short essay answer by applying the K-Means Clustering to group each question's topic and Latent Semantic Analysis to make the assessment. The system is developed to help facilitate the examination of essay answers that are currently still being done manually. The development of the system itself is done by using Python programming language. The test scenarios were carried out by varying the types of hiragana and romaji input also the process of stopwords elimination. From the results obtained and the analysis carried out, the form or type of text input used and the use of parameter such as stopwords affect the accuracy of the assessment. The developed automatic essay grading system was able to obtain the highest accuracy rate of 89% by using input in the form of romaji letters and without the stopwords elimination process.
KW - Answer categorization
KW - Essay grading
KW - K-Means
KW - Latent semantic analysis
KW - Tf-idf
UR - http://www.scopus.com/inward/record.url?scp=85076495719&partnerID=8YFLogxK
U2 - 10.1109/QIR.2019.8898271
DO - 10.1109/QIR.2019.8898271
M3 - Conference contribution
T3 - 2019 16th International Conference on Quality in Research, QIR 2019 - International Symposium on Electrical and Computer Engineering
BT - 2019 16th International Conference on Quality in Research, QIR 2019 - International Symposium on Electrical and Computer Engineering
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th International Conference on Quality in Research, QIR 2019
Y2 - 22 July 2019 through 24 July 2019
ER -