Perbandingan Kinerja Algoritma Klasifikasi Dalam Deteksi Spam Email (Studi Kasus Menggunakan Metode Naive Bayes Dan Support vector Machines)
Keywords:
Keywords: Data Mining; Comparison; Classification; Email Spam; Naïve Bayes; Support Vector Machines and Rapid MinerAbstract
This research aims to compare the performance of classification algorithms commonly used in email spam detection, namely the Naive Bayes Method and Support Vector Machines. The main objective of this research is to determine the most effective and efficient algorithm in identifying spam emails. The method used in this research is an experiment using an email data set consisting of emails categorized as spam and non-spam. The data is used to train and test the selected classification algorithm. The performance criteria used in the comparison are accuracy, precision, recall, and F1-score.
The research results show that both classification algorithms provide good performance in email spam detection. However, there are significant differences in the performance of each algorithm. The Naive Bayes method performs well in terms of accuracy, but tends to have lower precision. Support Vector Machines provide balanced performance in all tested performance criteria.
Based on the research results, it can be concluded that in the context of email spam detection, the Support Vector Machines algorithm tends to be the most effective and efficient choice. However, this research also shows that the selection of algorithms must be adjusted to the characteristics of the data used and the desired goals.
Keywords: Data Mining; Comparison; Classification; Email Spam; Naïve Bayes; Support Vector Machines and Rapid Miner
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