Phishing is an evolving form of cybercrime that targets users' sensitive information through URL manipulation. Conventional detection methods such as blacklists and signature-based approaches have become increasingly inadequate in addressing the dynamic variations of modern phishing attacks. This study evaluates the effectiveness of Recurrent Neural Network (RNN) variants, such Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit (GRU), in detecting phishing threats based on URL data. The methodology involves a Systematic Literature Review (SLR) of scholarly publications from the past ten years, complemented by experimental implementation of the models using a public dataset from Kaggle. Literature findings show that Bi-LSTM consistently achieves the highest accuracy, up to 99%, while GRU stands out for its computational efficiency. Experimental results support these findings, with Bi-LSTM achieving an accuracy of 96.22%, GRU 96.29%, and LSTM 95.43%. Classification metrics indicate that RNN-based models perform very well in detecting benign and defacement URLs, although their performance on phishing URLs remains challenged, particularly in terms of recall. These results confirm that RNNs remain a promising approach for phishing detection systems, especially when integrated into hybrid models with complementary architectures. This study is expected to provide a foundation for developing precise and adaptive AI systems to combat increasingly sophisticated phishing threats.

In the rapidly developing digital era, social media such as Twitter has become part of everyday life and facilitates the rapid dissemination of information, including information about criminals. This research aims to analyze public sentiment towards information about criminals spread on Twitter using the Naive Bayes algorithm. This algorithm was chosen because of its simplicity and effectiveness in text classification. Data was collected through a crawling process from Twitter, followed by a preprocessing stage to remove noise. The research results show that public sentiment towards information about criminals on Twitter is divided into three categories: positive, neutral and negative. After classification, it was found that neutral sentiment increased significantly to 63.4%, while positive and negative sentiment decreased to 10.5% and 26.1%. These findings indicate that people tend to be more careful in reacting to sensitive information. This research provides important insights for related parties in managing information about criminals on social media and can be a reference for developing further policies and strategies.