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All Journal Journal of Mathematics Education and Application (JMEA) BAREKENG: Jurnal Ilmu Matematika dan Terapan MAJAMATH: Jurnal Matematika dan Pendidikan Matematika MES: Journal of Mathematics Education and Science Indonesian Journal of Education and Mathematical Science PHI: Jurnal Pendidikan Matematika Al'adzkiya International of Education and Sosial (AIoES) Journal Journal Mathematics Education Sigma [JMES] Education Achievement: Journal of Science and Research Jurnal Abdimas Kartika Wijayakusuma Indo-MathEdu Intellectuals Journal MATH-EDU: Jurnal Ilmu Pendidikan Matematika Tsaqila Jurnal Pendidikan dan Teknologi OMEGA: Jurnal Keilmuan Pendidikan Matematika Hanif Journal of Information Systems Jurnal Tadris Matematika (JTMT)
Azis, Zainal
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Religion Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Energy Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Journal : Hanif Journal of Information Systems

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Predicting The Risk of Online Sales Fraud with The Naïve Bayes Approach on Facebook Social Media Pasha, Leony Ayu Diah; Azis, Zainal
Hanif Journal of Information Systems Vol. 2 No. 2 (2025): February Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/hanif.v2i2.41

Abstract

The rapid development of digital shopping media is accompanied by increasing cases of online fraud, especially through social media platforms such as Facebook. This study aims to develop a prediction model for the risk of online sales fraud using the Naïve Bayes algorithm. The data used is the data of buying and selling transactions that occur through the Facebook marketplace. The data has been collected on the Kaggle platform so that it can be used directly. Data in the form of extracted features include seller characteristics, products sold, number of transactions, device usage and other fraud indicators. Important features that affect the potential for fraud are identified and used in the machine learning process. The results of the study show that the Naïve Bayes model is able to provide accurate predictions in identifying the risk of online sales fraud, with a satisfactory accuracy rate of 95%. The results of the study are expected to contribute to the development of a more effective fraud detection system and increase user confidence in making online transactions.
Co-Authors Afnaria, Afnaria Akbar, Rahmat Ammy, Putri Maisyarah Annisa, Dewi Sry Ardiningtyas, Mesayu Azmi, Muhammad Bima Dedy Kiswanto Fajar, Maulana Haloho, Elysha Jefirayani Hasibuan, Ahmad Riady Ikhlas Hamzani Irvan Irvan Irvan, Irvan Khoiruddin Matondang Lubis, Tri Resti Andini Lubis, Ulfa Annisa Marah Doly Nasution Nasution, Asrindah Nurhayati Nurhayati Nurlinda, Epi Nurul Maulida Surbakti Panggabean, Suvriadi Pasha, Leony Ayu Diah purba, oktaviana nirmala Puspita, Ananda Putri, Linda Syah Rahim, Rani Risna Mira Bella Saragih Rizka Fahruza Siregar RR. Ella Evrita Hestiandari Siregar, Rosliana Siswadi Siswadi Syahlan, Syahlan Tua Halomoan Harahap, Tua Halomoan Yusma, Ira