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Analisis perbandingan algoritma Naïve Bayes, k-Nearest Neighbor dan Neural Network untuk permasalahan class-imbalanced data pada kasus credit card fraud dataset

Teknologi: Jurnal Ilmiah Sistem Informasi

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Title Analisis perbandingan algoritma Naïve Bayes, k-Nearest Neighbor dan Neural Network untuk permasalahan class-imbalanced data pada kasus credit card fraud dataset
 
Creator Sahroni, Mita Yuanika
Setifani, Niken Ayu
Fitriana, Devinta Nurul
 
Description The high public interest in transactions using credit cards in the banking sector has the potential for higher credit card fraud. This study uses a credit card fraud dataset that consisting of 284,807 data obtained from Kaggle. The dataset in this study is class-imbalanced data with a comparison between the major class of 99.8% and the minor class of 0.2%. This class-imbalanced data problem will be solved by applying undersampling. In order to determine the performance of the classification algorithm that is most suitable for solving class-imbalanced data problems, a comparison of the Naïve Bayes, k-Nearest Neighbor (kNN) and Neural Network algorithms will be carried out. The t-test in this study was conducted to determine the significance of differences between algorithms. Algorithm performance evaluation uses accuracy and AUC (area under the curve) values. The test results in this study is Neural Network has better performance than other algorithms because it has the highest accuracy value of 93.59% and AUC value of 0.977. Based on the t-test results, the Neural Network with k-NN has a significant difference, in contrast to the Neural Network with Naïve Bayes there is no significant difference.
 
Publisher Universitas Pesantren Tinggi Darul 'Ulum (Unipdu) Jombang
 
Contributor —
 
Date 2021-06-09
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier https://journal.unipdu.ac.id/index.php/teknologi/article/view/2393
10.26594/teknologi.v11i2.2393
 
Source Teknologi: Jurnal Ilmiah Sistem Informasi; Vol 11, No 2 (2021): July; 69-73
TEKNOLOGI: Jurnal Ilmiah Sistem Informasi; Vol 11, No 2 (2021): July; 69-73
2527-3671
2087-8893
 
Language eng
 
Relation https://journal.unipdu.ac.id/index.php/teknologi/article/view/2393/1256
https://journal.unipdu.ac.id/index.php/teknologi/article/downloadSuppFile/2393/332
https://journal.unipdu.ac.id/index.php/teknologi/article/downloadSuppFile/2393/333
 
Rights Copyright (c) 2021 Mita Yuanika Sahroni, Niken Ayu Setifani, Devinta Nurul Fitriana
http://creativecommons.org/licenses/by-nc-sa/4.0