The Combination of Logistic Regression and Gradient Boost Tree for Email Spam Detection

Convenience communicate in this era making trends throughout the world. Email is one of many tools that still used for communicating. In the activity of sending-receiving email, some irresponsible people often send spam messages to recipients only for profit. The process of spam filtering is still continue developed to reduce the number of spam emails. In this research, we propose Logistic Regression with Select by Weight and Gradient Boost Tree for developed spam filtering to make spam filters more advanced. The model has been built using Logistic Regression with Select by Weight and Gradient Boost Tree showing a good result. Accuracy generated from the mentioned models is 95.13%.