Universitas Internasional Batam ANALISIS KEAMANAN SISTEM INFORMASI MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE TERHADAP PENGGUNA SHOPEE

Abstrak

 Digital transactions depend heavily on information system security, especially on e-commerce sites like Shopee, which has a constantly expanding user base in Indonesia. The possibility of security risks, such as account abuse, unauthorized access, and questionable user behavior, rises along with the volume of online buying. The purpose of this study is to examine information system security-related aspects based on Shopee users' experiences and behavior. An online survey was used to gather data on demographics, usage trends, user awareness, and experiences with questionable activity. The Support Vector Machine (SVM) algorithm was used as the main classification technique in a quantitative manner. Normalization, attribute selection, and modeling using the Orange Data Mining platform were among the preprocessing steps used to the gathered data. Finding behavioral patterns that might be connected to the prevalence of suspicious activity on Shopee accounts is the main goal of this study. The research aims to give a preliminary understanding of how user traits and behavioral elements can function as indications of possible information system security concerns by utilizing SVM. It is anticipated that the results of this study will aid in the creation of machine learning-based early detection strategies for e-commerce platforms.