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Jurnal SINTA 2

SOFTWARE DEFECT PREDICTION USING PCA BASED RECURRENT NEURAL NETWORK

JUTI: Jurnal Ilmiah Teknologi Informasi · Vol. 22 No. 1 (2024) · pp. 23–31

DOI
10.12962/j24068535.v22i1.a1199
ISSN
2406-8535, 1412-6389
Published
2024-01-31
Publisher
Institut Teknologi Sepuluh Nopember
Language
eng

Abstract

Software quality is one of the important phases in software development. Software quality assesses the usability and quality of the software developed. Defect prediction early in software development helps in software quality assurance by reducing software defects that may occur. With good predictions, it will provide additional benefits in terms of resource and cost efficiency. The researchers in this study have proposed a software defect prediction method that utilizes a Recurrent Neural Network (RNN) based on Principal Component Analysis (PCA). The dataset used is the PROMISE dataset, namely JM1, CM1, PC1, KC1, and KC2. The test results showed that the PCA-RNN method was successfully applied. For the highest accuracy on the PC1 dataset, with an accuracy of 93.99% with the division of training data by testing data (70:30).

Collaboration network of the authors

20 authors · 36 co-authorship links

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  • Institut Teknologi Sepuluh Nopember
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
I Gusti Lanang Agung Oka Cahyadi PradiptaAffiliation unknown1
Ahmad SaikhuInstitut Teknologi Sepuluh Nopember1
Diana PurwitasariInstitut Teknologi Sepuluh Nopember1
Daniel SiahaanInstitut Teknologi Sepuluh Nopember1
Dian SaputraAffiliation unknown1
Esti YuniarAffiliation unknown1
Joko Lianto BulialiInstitut Teknologi Sepuluh Nopember1
Dwi SunaryonoInstitut Teknologi Sepuluh Nopember1
Hazna At ThooriqohInstitut Teknologi Sepuluh Nopember1
Tiara Nur AnnisaInstitut Teknologi Sepuluh Nopember1
Ary Mazharuddin ShiddiqiInstitut Teknologi Sepuluh Nopember1
Muhammad Faisal FananiInstitut Teknologi Sepuluh Nopember1
Amelia Devi Putri AriyantoInstitut Teknologi Sepuluh Nopember1
Lutfiyatul †̃AzizahInstitut Teknologi Sepuluh Nopember1
Wahyu Fajar SetiawanAffiliation unknown1
Ilham Putra AriatamaAffiliation unknown1
Muhammad AlfianAffiliation unknown1

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://juti.if.its.ac.id/index.php/juti/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en">SOFTWARE DEFECT PREDICTION USING PCA BASED RECURRENT NEURAL NETWORK</dc:title>
	<dc:creator xml:lang="en">Kusnanti, Eka Alifia</dc:creator>
	<dc:creator xml:lang="en">Vantie, Lauretha Devi Fajar</dc:creator>
	<dc:creator xml:lang="en">Yuhana, Umi Laili</dc:creator>
	<dc:description xml:lang="en">Software quality is one of the important phases in software development. Software quality assesses the usability and quality of the software developed. Defect prediction early in software development helps in software quality assurance by reducing software defects that may occur. With good predictions, it will provide additional benefits in terms of resource and cost efficiency. The researchers in this study have proposed a software defect prediction method that utilizes a Recurrent Neural Network (RNN) based on Principal Component Analysis (PCA). The dataset used is the PROMISE dataset, namely JM1, CM1, PC1, KC1, and KC2. The test results showed that the PCA-RNN method was successfully applied. For the highest accuracy on the PC1 dataset, with an accuracy of 93.99% with the division of training data by testing data (70:30).</dc:description>
	<dc:publisher xml:lang="en">Institut Teknologi Sepuluh Nopember</dc:publisher>
	<dc:date>2024-01-31</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">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://juti.if.its.ac.id/index.php/juti/article/view/1199</dc:identifier>
	<dc:identifier>10.12962/j24068535.v22i1.a1199</dc:identifier>
	<dc:source xml:lang="en">JUTI: Jurnal Ilmiah Teknologi Informasi; Vol. 22, No. 1, January 2024; 23-31</dc:source>
	<dc:source>2406-8535</dc:source>
	<dc:source>1412-6389</dc:source>
	<dc:source>10.12962/j24068535.v22i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://juti.if.its.ac.id/index.php/juti/article/view/1199/508</dc:relation>
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