Comparison of Deep Neural Network (DNN) and Long Short-Term Memory (LSTM) Algorithms in Email Spam Clustering

Abstract

Sending correspondence through electronic media, also known as e-mail, is a common practice in everyday life. Nonetheless, with the increasing use of e-mail, there is also more misuse of e-mail in the field of correspondence. This research aims to look at the presentation of two algorithms related to email spam clustering, specifically Deep Neural Network (DNN) and Long Short-term Memory (LSTM). The testing process involves determining the model architecture, parameter initialisation. Evaluation is done by comparing accuracy, recall, precision, and F1-score metrics. Testing the performance of DNN and LSTM using Confusion Matrix shows that the DNN algorithm is significantly superior and efficient than the LSTM algorithm. DNN stands out in achieving a higher level of accuracy, where its ability to extract complex features proves more effective. It was found that the DNN algorithm has an accuracy value of 96.32%, recall 96.09%, Precision 96.64% and F1-Score 96.15% while the LSTM algorithm has an accuracy value of 89.20%, recall 87.36%, Precision 90.88% and F1-Score 88.97%.