Klasifikasi Malicious URL Menggunakan Algoritma Improved Random Forest dan Random Forest Berbasis Web

URLs are very much on the network of computer systems. Moreover, nowadays all activities use an online system. Starting from social media, and marketplaces to group chat applications. An early prevention system malicious URL attacks is needed counteract large number circulating in Previously detection based blacklisting UURLs Previously, Blacklisting Heuristic could not recognize new type without first being analyzed. For this reason, a technique detect using machine learning. The lack learning that it 100% able precisely. This study will improved random forest approach with as classifier URLs. Improved Random Forest used evaluator features filter instances improve accuracy ordinary forests. concluded both methods have value above 98%.