Analisis perbandingan algoritma Naïve Bayes, k-Nearest Neighbor dan Neural Network untuk permasalahan class-imbalanced data pada kasus credit card fraud dataset
DOI:
https://doi.org/10.26594/teknologi.v11i2.2393Abstract
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.References
Brown, I., & Mues, C. (2012). An experimental comparison of classification algorithms for imbalanced credit scoring data sets. Expert Systems with Applications, 39(3), 3446-3453.
Chamidah, N., Santoni, M. M., & Matondang, N. (2020). Pengaruh Oversampling pada Klasifikasi Hipertensi dengan Algoritma Naïve Bayes, Decision Tree, dan Artificial Neural Network (ANN). Jurnal RESTI (Rekayasa Sistem dan Teknologi, 4(4), 635-641.
Devita, R. N., Herwanto, H. W., & Wibawa, A. P. (2018). Perbandingan Kinerja Metode Naive Bayes dan K-Nearest Neighbor untuk Klasifikasi Artikel Berbahasa Indonesia. Jurnal Teknologi Informasi dan Ilmu Komputer, 5(4), 427-434.
Hairani, H., Saputro, K. E., & Fadli, S. (2020). K-means-SMOTE untuk menangani ketidakseimbangan kelas dalam klasifikasi penyakit diabetes dengan C4.5, SVM, dan naive Bayes. Jurnal Teknologi dan Sistem Komputer, 8(2), 89-93.
Jayadianti, H., Cahyadi, T. A., Amri, N. A., & Pitayandanu, M. F. (2020). Metode Komparasi Artificial Neural Network pada Prediksi Curah Hujan - Literature Review. Jurnal Tekno Insentif, 14(2), 48-53.
Karni, S. (2000). Auditing: Audit Khusus & Audit Forensik dalam praktik. Jakarta: Universitas Indonesia.
Karyono, K. (2013). Forensic Fraud. Andi: Yogyakarta.
Khatri, S., Arora, A., & Agrawal, A. P. (2020). Supervised Machine Learning Algorithms for Credit Card Fraud Detection: A Comparison. 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence) (pp. 680-683). Noida, India: IEEE.
Larose, D. T., & Larose, C. D. (2014). Discovering Knowledge in Data: An Introduction to Data Mining. John Wiley & Sons.
Prasetio, R. T., & Pratiwi, P. (2015). Penerapan Teknik Bagging pada Algoritma Klasifikasi untuk Mengatasi Ketidakseimbangan Kelas Dataset Medis. Jurnal Informatika, 2(2), 395-403.
Rahman, A., Rahmat, F., Fariqi, M. Y., & Adi, S. (2020). Metode Naive Bayes untuk Menganalisis Akurasi Sentimen Komentar di Youtube. Jurnal EECCIS (electrics, electronics, communications, controls, informatics, systems), 14(1), 31–34.
Religia, Y., Nugroho, A., & Hadikristanto, W. (2021). Analisis Perbandingan Algoritma Optimasi pada Random Forest untuk Klasifikasi Data Bank Marketing. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 5(1), 187-192.
Shin, H., & Cho, S. (2006). Response modeling with support vector machines. Expert Systems with Applications, 30(4), 746-760.
Shirodkar, N., Mandrekar, P., Mandrekar, R. S., Sakhalkar, R., Kumar, K. M., & Aswale, S. (2020). Credit Card Fraud Detection Techniques – A Survey. International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE). Vellore, India: IEEE.
Zareapoor, M., Seeja, K. R., & Alam, M. A. (2012). Analysis of Credit Card Fraud Detection Techniques: based on Certain Design Criteria. International Journal of Computer Applications, 52(3), 35-42.
Downloads
Published
Issue
Section
License
Please find the rights and licenses in Teknologi: Jurnal Ilmiah Sistem Informasi. By submitting the article/manuscript of the article, the author(s) agree with this policy. No specific document sign-off is required.
1. License
The non-commercial use of the article will be governed by the Creative Commons Attribution license as currently displayed on Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
2. Author(s)' Warranties
The author warrants that the article is original, written by stated author(s), has not been published before, contains no unlawful statements, does not infringe the rights of others, is subject to copyright that is vested exclusively in the author and free of any third party rights, and that any necessary written permissions to quote from other sources have been obtained by the author(s).
3. User/Public Rights
Register's spirit is to disseminate articles published are as free as possible. Under the Creative Commons license, Register permits users to copy, distribute, display, and perform the work for non-commercial purposes only. Users will also need to attribute authors and Register on distributing works in the journal and other media of publications. Unless otherwise stated, the authors are public entities as soon as their articles got published.
4. Rights of Authors
Authors retain all their rights to the published works, such as (but not limited to) the following rights;
Copyright and other proprietary rights relating to the article, such as patent rights,
The right to use the substance of the article in own future works, including lectures and books,
The right to reproduce the article for own purposes,
The right to self-archive the article (please read out deposit policy),
The right to enter into separate, additional contractual arrangements for the non-exclusive distribution of the article's published version (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal (Register: Jurnal Ilmiah Teknologi Sistem Informasi).
5. Co-Authorship
If the article was jointly prepared by more than one author, any authors submitting the manuscript warrants that he/she has been authorized by all co-authors to be agreed on this copyright and license notice (agreement) on their behalf, and agrees to inform his/her co-authors of the terms of this policy. Register will not be held liable for anything that may arise due to the author(s) internal dispute. Register will only communicate with the corresponding author.
6. Royalties
Being an open accessed journal and disseminating articles for free under the Creative Commons license term mentioned, author(s) aware that Register entitles the author(s) to no royalties or other fees.
7. Miscellaneous
Register will publish the article (or have it published) in the journal if the article’s editorial process is successfully completed. Register's editors may modify the article to a style of punctuation, spelling, capitalization, referencing and usage that deems appropriate. The author acknowledges that the article may be published so that it will be publicly accessible and such access will be free of charge for the readers as mentioned in point 3.
