Fraud Accounts Identification Modelling on Multi-Platform E-Commerce
Keywords:
Cybercrime, E-Commerce Fraud, Naïve Bayes, decision tree, K-NN, multi-platformAbstract
Nowadays, cybercrime is increasingly prevalent in society. Based on data compiled by the Indonesia National Police, the number of cybercrimes increases by 6.46% annually, with online fraud as the most reported crime with 7.892 cases or 44.40% out of the total cases handled. The modus operandi in online fraud primarily uses manipulated profile account to gain the victims' trust. Therefore, it is necessary to have a common detection model for fraud accounts on multi-platform e- commerce to avoid online fraud. This research uses the Naïve Bayes classification, Decision Tree, and K-NN as the modeling algorithms. The classification test result showed that the optimal performance with the highest accuracy differs among the platform. The green platform reaches the highest accuracy using the K-NN algorithm (90.51%), the red platform went to the optimal performance using the Decision Tree algorithm (96.89%), and the multi-platform reached the optimal performance using the Naïve Bayes algorithm (90.02%).
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Copyright (c) 2025 Grawas Sugiharto, Yudistira Dwi Wardhana Asnar (Author)
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