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Open access Jurnal SINTA 2

ESSAY ANSWER CLASSIFICATION WITH SMOTE RANDOM FOREST AND ADABOOST IN AUTOMATED ESSAY SCORING

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 17 No. 4 (2023) · pp. 359–370

DOI
10.22146/ijccs.82548
ISSN
2460-7258, 1978-1520
Published
2023-10-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Automated essay scoring (AES) is used to evaluate and assessment student essays are written based on the questions given. However, there are difficulties in conducting automatic assessments carried out by the system, these difficulties occur due to typing errors (typos), the use of regional languages, or incorrect punctuation. These errors make the assessment less consistent and accurate. Based on the dataset analysis that has been carried out, there is an imbalance between the number of right and wrong answers, so a technique is needed to overcome the data imbalance. Based on the literature, to overcome these problems, the Random Forest and AdaBoost classification algorithms can be used to improve the consistency of classification accuracy and the SMOTE method to overcome data imbalances.The Random Forest method using SMOTE can achieve an F1 measure of 99%, which means that the hybrid method can overcome the problem of imbalanced datasets that are limited to AES. The AdaBoost model with SMOTE produces the highest F1 measure reaching 99% of the entire dataset. The structure of the dataset is something that also affects the performance of the model. So the best model obtained in this study is the Random Forest model with SMOTE.

Keywords

Collaboration network of the authors

6 authors · 6 co-authorship links

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  • Universitas Gadjah Mada
  • Different organisations
  • Same organisation
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Co-authorOrganisationJoint articles
Reza PulunganUniversitas Gadjah Mada1
Muhammad Asghar NazalUniversitas Gadjah Mada1
Kartika SariUniversitas Gadjah Mada1
Hirzen HasfaniUniversitas Gadjah Mada1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">ESSAY ANSWER CLASSIFICATION WITH SMOTE RANDOM FOREST AND ADABOOST IN AUTOMATED ESSAY SCORING</dc:title>
	<dc:creator>Satria, Wilia</dc:creator>
	<dc:creator>Riasetiawan, Mardhani</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science</dc:subject>
	<dc:subject xml:lang="en-US">SMOTE; Random Forest; AdaBoost; Automated Essay Scoring</dc:subject>
	<dc:description xml:lang="en-US"> Automated essay scoring (AES) is used to evaluate and assessment student essays are written based on the questions given. However, there are difficulties in conducting automatic assessments carried out by the system, these difficulties occur due to typing errors (typos), the use of regional languages , or incorrect punctuation. These errors make the assessment less consistent and accurate. Based on the dataset analysis that has been carried out, there is an imbalance between the number of right and wrong answers, so a technique is needed to overcome the data imbalance. Based on the literature, to overcome these problems, the Random Forest and AdaBoost classification algorithms can be used to improve the consistency of classification accuracy and the SMOTE method to overcome data imbalances.The Random Forest method using SMOTE can achieve an F1 measure of 99%, which means that the hybrid method can overcome the problem of imbalanced datasets that are limited to AES. The AdaBoost model with SMOTE produces the highest F1 measure reaching 99% of the entire dataset. The structure of the dataset is something that also affects the performance of the model. So the best model obtained in this study is the Random Forest model with SMOTE.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2023-10-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/82548</dc:identifier>
	<dc:identifier>10.22146/ijccs.82548</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 17, No 4 (2023): October; 359-370</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/82548/37708</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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