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Increasing Accuracy of The Random Forest Algorithm Using PCA and Resampling Techniques with Data Augmentation for Fraud Detection of Credit Card Transaction

Journal of Advances in Information Systems and Technology · Vol. 4 No. 1 (2022) · hlm. 60–76

DOI
10.15294/jaist.v4i1.60865
ISSN
2715-999X, 2714-9714
Terbit
2022-12-08
Penerbit
Department of Computer Science, Faculty of Mathematics and Natural Sciences, Universitas Negeri Semarang
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

The credit-card transaction analysis uses a random forest algorithm as an algorithm for the classification process. The problem faced from the classification process using credit card fraud filing dataset fraud is an imbalanced data that causes an imbalanced data alignment on the model results from data training. To resolve the problem, a combination of PCA methods and resampling techniques with data augmentation for the optimum process on random forest classification algorithms. The PCA method is used in the preprocessing stage to do the process of transforming data into numerical data and resampling techniques and data augmentation are used in data resamples to bring the data to a balance. The data used is a data card fraud of Europe that has 284807 transactions. Model accuracy measurement was implemented using confusion matrix. The highest accuracy results from a random forest combination using PCA and resampling techniques with data augmentation of 99.9976%.

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  • State University of Semarang
  • International Islamic University Malaysia
  • Universitas PGRI Palembang
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Rekan penulisOrganisasiArtikel bersama
Endang SugihartiState University of Semarang5
Florentina Yuni AriniState University of Semarang3
Rochmad RochmadInternational Islamic University Malaysia3
Amin SuyitnoState University of Semarang3
Alamsyah AlamsyahState University of Semarang2
Novanka Agnes SekartajiState University of Semarang2
Irfan Fajar MuttaqinState University of Semarang2
Ramadhan Ridho ArrohmanState University of Semarang2
Dwijanto DwijantoState University of Semarang2
Putriaji HendikawatiState University of Semarang2
Budi PrasetiyoState University of Semarang1
Much Aziz MuslimState University of Semarang1
Jumanto JumantoState University of Semarang1
Rofik RofikState University of Semarang1
Arif WidiyatmokoState University of Semarang1
Stephani Diah PamelasariState University of Semarang1
Arka YanitamaState University of Semarang1
Melissa Salma DarmawanState University of Semarang1
Aji SaputraUniversitas PGRI Palembang1
Desy Fitria AstutianingtyasAfiliasi tidak diketahui1
Syafa Nabilah SyahputraState University of Semarang1
Anggyi Trisnawan PutraState University of Semarang1
Athoillah AthoillahState University of Semarang1
Kholiq BudimanAfiliasi tidak diketahui1
Anggyi Trisnawan PutraAfiliasi tidak diketahui1
zaenal abidinAfiliasi tidak diketahui1
Anggoro Sakti AnindhitoState University of Semarang1
Fajar SujitoAfiliasi tidak diketahui1
Anita Wahyu MelindaAfiliasi tidak diketahui1
Isa AkhlisState University of Semarang1
Achmad Wahyu HidayatAfiliasi tidak diketahui1
Dian Rahman SyahputraAfiliasi tidak diketahui1
Safit FirmansyahAfiliasi tidak diketahui1
Anggil AgustaAfiliasi tidak diketahui1
Fachrizal Ahdy AndoyoState University of Semarang1
Margareta Ayu RiantikState University of Semarang1
Dandi Indra WijayaAfiliasi tidak diketahui1
Hanif Nur CahyaniAfiliasi tidak diketahui1

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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/sju/index.php/jaist/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc)
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	<dc:title xml:lang="en-US">Increasing Accuracy of The Random Forest Algorithm Using PCA and Resampling Techniques with Data Augmentation for Fraud Detection of Credit Card Transaction</dc:title>
	<dc:creator>Tamtama, Andhika Seno</dc:creator>
	<dc:creator>Arifudin, Riza</dc:creator>
	<dc:description xml:lang="en-US">



The credit-card transaction analysis uses a random forest algorithm as an algorithm for the classification process. The problem faced from the classification process using credit card fraud filing dataset fraud is an imbalanced data that causes an imbalanced data alignment on the model results from data training. To resolve the problem, a combination of PCA methods and resampling techniques with data augmentation for the optimum process on random forest classification algorithms. The PCA method is used in the preprocessing stage to do the process of transforming data into numerical data and resampling techniques and data augmentation are used in data resamples to bring the data to a balance. The data used is a data card fraud of Europe that has 284807 transactions. Model accuracy measurement was implemented using confusion matrix. The highest accuracy results from a random forest combination using PCA and resampling techniques with data augmentation of 99.9976%.



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	<dc:publisher xml:lang="en-US">Department of Computer Science, Faculty of Mathematics and Natural Sciences, Universitas Negeri Semarang</dc:publisher>
	<dc:date>2022-12-08</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">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/sju/jaist/article/view/60865</dc:identifier>
	<dc:identifier>10.15294/jaist.v4i1.60865</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Advances in Information Systems and Technology; Vol 4 No 1 (2022): April; 60-76</dc:source>
	<dc:source>2715-999X</dc:source>
	<dc:source>2714-9714</dc:source>
	<dc:source>10.15294/jaist.v4i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/sju/jaist/article/view/60865/23149</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 Journal of Advances in Information Systems and Technology</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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