<![CDATA[The expansion of e-commerce in Indonesia has made information system security a crucial concern, especially on sites like Shopee that see a lot of user activity and transaction volumes. Potential security hazards, such as account misuse, unauthorized access, and suspicious activity, are increased by the volume of online transactions. Therefore, in order to comprehend the elements linked to security threats based on user characteristics and behavioral patterns, an analytical approach is necessary. The purpose of this study is to apply machine learning to examine security risk tendencies among Shopee users. A standardized questionnaire addressing demographic factors, usage frequency, security awareness levels, and experiences with questionable activity was used to gather data from 101 active users. Data cleaning, label encoding, Min–Max normalization, and feature selection were among the steps in the data processing procedure. The classification model used was the Support Vector Machine (SVM) technique with a Radial Basis Function (RBF) kernel. The creation of a security risk analysis model based on user perceptions and behavioral aspects rather than system log or transactional data is what makes this study unique. By using non-technical indications as predictive factors in e-commerce platforms, this method provides an alternate viewpoint for spotting possible security threats.]]>
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