Melola Discovery

← Melola Discovery

Open access Jurnal SINTA 2

Automatic Essay Scoring Using Data Augmentation in Bahasa Indonesia

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 16 No. 4 (2022) · pp. 401–410

DOI
10.22146/ijccs.76396
ISSN
2460-7258, 1978-1520
Published
2022-10-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Essay is one of the assessments to find out the abilities of students in depth. UKARA is an automatic essay scoring development that combines NLP and machine learning. This study uses the datasets provided for the UKARA challenge which consists of 2 types, datasets A and B. The dataset provided is still small for the model creation process so that it is one of the causes of the resulting model is not optimal. This research focuses on the process of adding or augmenting data using EDA (Easy Data Augmentation Techniques). There are four methods applied, namely Synonym Replacement (SR), Random Insertion (RI), Random Swab (RS), and Random Deletion (RD). The data is used for model creation by using the BiLSTM method. Performa model evaluated using confusion matrix with nilai accyouracy, precision, recall dan f-measure.The results showed that the dataset A without augmentation using k-fold cross validation produced the highest accuracy value with a value of 85.07%. While the results in data B show EDA insert with k-fold cross validation of 72.78%.

Keywords

Collaboration network of the authors

22 authors · 43 co-authorship links · 15 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Gadjah Mada
  • Syarif Hidayatullah State Islamic University Jakarta
  • Universitas Dian Nuswantoro
  • Udayana University
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Ghulam ImanuddinUniversitas Gadjah Mada1
Suci Karunia PrilistyaUniversitas Gadjah Mada1
Azhari SnUniversitas Gadjah Mada1
Winda YulitaUniversitas Gadjah Mada1
Nur Azizul HaqimiUniversitas Gadjah Mada1
Nur RokhmanUniversitas Gadjah Mada1
Edi WinarkoUniversitas Gadjah Mada1
Sholikin Ady ChandraUniversitas Gadjah Mada1
Mhd. Theo Ari BangsaUniversitas Gadjah Mada1
Yohanes SuyantoUniversitas Gadjah Mada1
Khabib MustofaUniversitas Gadjah Mada1
Amalia Nur AnggraeniUniversitas Gadjah Mada1
Huda MustakimUniversitas Gadjah Mada1
Dika Permana PutraUniversitas Gadjah Mada1
Muthia Farah HanifaSyarif Hidayatullah State Islamic University Jakarta1
Anugrah Tri RamadhanUniversitas Dian Nuswantoro1
Ni’Matul HusnaUniversitas Gadjah Mada1
Nabila Apriliana WidiyonoUniversitas Gadjah Mada1
Rhamdan Syahrul MubarakUniversitas Gadjah Mada1
Adisti Anjani PutriUdayana University1

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) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Automatic Essay Scoring Using Data Augmentation in Bahasa Indonesia</dc:title>
	<dc:creator>Fadilah, Nur</dc:creator>
	<dc:creator>Priyanta, Sigit</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science</dc:subject>
	<dc:subject xml:lang="en-US">Test Brief Description; Data Augmentation; Fasttext; BiLSTM</dc:subject>
	<dc:description xml:lang="en-US">Essay is one of the assessments to find out the abilities of students in depth.  UKARA is an automatic essay scoring development that combines NLP and machine learning.  This study uses the datasets provided for the UKARA challenge which consists of 2 types, datasets A and B. The dataset provided is still small for the model creation  process so that it is one of the causes of the resulting model is not optimal. This research focuses on the process of adding or augmenting data using EDA (Easy Data Augmentation Techniques). There are four methods applied, namely Synonym Replacement (SR), Random Insertion (RI), Random Swab (RS), and Random Deletion (RD).  The data is used for model creation by using the BiLSTM method. Performa model evaluated using confusion matrix with nilai accyouracy, precision, recall dan f-measure.The results showed that the dataset A without augmentation using k-fold cross validation produced the highest accuracy value with a value of 85.07%. While the results in data B show EDA insert with k-fold cross validation of 72.78%.</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>2022-10-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/76396</dc:identifier>
	<dc:identifier>10.22146/ijccs.76396</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 16, No 4 (2022): October; 401-410</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/76396/34988</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 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>
</oai_dc:dc>