EVALUASI EFEKTIVITAS SISTEM DETEKSI PENIPUAN BERBASIS AI MENGGUNAKAN METODE REGRESI LOGISTIK UNTUK MENINGKATKAN KEAMANAN TRANSAKSI PADA STARTUP FINANCE
DOI:
https://doi.org/10.31949/jensitec.v10i02.9818Abstract
This research evaluates the effectiveness of an AI-based fraud detection system in a finance startup using logistic regression methods. The primary objective is to assess whether this model is feasible for enhancing transaction security. The research methodology employed is quantitative, utilizing historical transaction data. The test results indicate that the logistic regression model can accurately predict the company's financial conditions, with an accuracy of 95%. However, there are challenges in detecting fraudulent transactions, as evidenced by the low recall for fraudulent transactions. The model evaluation was conducted using several performance metrics such as accuracy, precision, recall, and F1-score, as well as model fit tests including Hosmer and Lemeshow’s Goodness of Fit Test, Omnibus Test of Model Coefficients, and -2 log-likelihood. In conclusion, this model has the potential to enhance transaction security, but it still requires improvements to more effectively detect fraudulent transactions
Keywords:
Fraud Detection, Effectiveness, Transaction Security, Logistic Regression, Finance StartupDownloads
References
Hidayatullah, K. M. S., & Sutabri, T. (2024). Pengembangan Sistem Pengklasifikasi e-mail Berbasis Kecerdasan Buatan untuk Deteksi Spam dan Phishing. IJM: Indonesian Journal of Multidisciplinary, 2(2).
Irawan, D., & Affan, M. W. (2020). Pengaruh privasi dan keamanan terhadap niat menggunakan payment Fintech. Jurnal Kajian Akuntansi, 4(1), 52-62.
Kartikasari, D. (2020). Analisis Faktor-Faktor yang Mempengaruhi Level Polusi Udara dengan Metode Regresi Logistik Biner. Mathunesa: Jurnal Ilmiah Matematika, 8(1), 55-59.
Prayogi, G. D. (2024). Penerapan Sistem Pengendalian Internal Pemerintah sesuai PP 60 Tahun 2008 dan Sistem Informasi Akuntansi Berbasis Artificial Intelligence Terhadap Kecenderungan Fraudulent Financial Reporting:(Studi Kasus pada Organisasi Sektor Publik di Kabupaten Gresik). Jurnal Ilmiah Raflesia Akuntansi, 10(1), 174-184.
Sari, M. K., & Sadriatwati, S. E. (2020). Analisis financial distress pada bank umum syariah melalui metode regresi logistik biner data panel. Jurnal Penelitian Ekonomi dan Akuntansi (JPENSI), 5(2).
Suhendra, M. A., Ispriyanti, D., & Sudarno, S. (2020). Ketepatan Klasifikasi Pemberian Kartu Keluarga Sejahtera Di Kota Semarang Menggunakan Metode Regresi Logistik Biner Dan Metode Chaid. Jurnal Gaussian, 9(1), 64-74.
Yusuf, M. F. M., Garusu, I. A., & Rauf, D. M. (2024). Sistem Penerapan Artificial Intelligence Dalam Akuntansi: Indonesia. Jurnal Ilmu Sosial Dan Pendidikan, 2(2), 01-07.
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 M. Raflie Akbar, Kgs M. Syarif Hidayatullah, Tata Sutabri
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
An author who publishes in the J-ENSITEC (Journal of Engineering and Sustainable Technology) agrees to the following terms:
- Author retains the copyright and grants the journal the right of first publication of the work simultaneously licensed under the Creative Commons Attribution-ShareAlike 4.0 License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal
- The author is able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book) with the acknowledgment of its initial publication in this journal.
- The author is permitted and encouraged to post his/her work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of the published work
