PERBANDINGAN ALGORITMA NAÏVE BAYES DAN SVM UNTUK ANALISIS PENYALAHGUNAAN KEJAHATAN CARDING
DOI:
https://doi.org/10.51401/jinteks.v5i1.2231Keywords:
Carding, Machine Learning, Naïve Bayes, and Support vector machine.Abstract
Carding is a type of cybercrime related to banking, or credit cards. This crime is a method of stealing credit card numbers from legitimate websites and spammers and using them for personal gain. Due to the difficulty in identifying this fraud issue, card victims often experience lingering suspicions when attempting to use credit card buying schemes. Based on this problem, researchers hope to be able to analyze card crime using two machine learning algorithms that help detect card crime: Naive Bayes and Support vector machine. Based on the results of our research, we can conclude that both the Naive Bayes algorithm and Support vector machines can be used to accurately predict carding crime exploits. However, support machine algorithms are more accurate than Naive Bayes. The Support Machine Algorithm produces 1's values ??that are 99% higher than Naive Bayes. This is because support vector machine classifiers provide high accuracy and work well.
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