Sentiment Analysis for Detecting Cyberbullying Using TF-IDF and SVM

Social media has become a new method of today’s communication in a new digitalize era. Children and adults have used social media a lot in interacting with others. Therefore social media has shifted conventional communication into digital one. This digital development on social media is a serious problem that must be faced because it has been found that there are more and more acts of cyberbullying. This act of cyberbullying can attack the psychic, causing depression up to suicide. The dangers of cyberbullying are troubling and cause concern to the community. Therefore, this study will analyze the sentiment on the comments contained on social media to find out the value of sentiment from comments on social media platforms. The comment data will be processed at the preprocessing stage, Term Frequency-Inverse Document Frequency (TF-IDF), and the Support Vector Machine (SVM) classification method. Comment data to be classified as 1500 data taken using crawling data through libraries in python programming and divided into 80% data training and 20% data testing. Based on the results of the test, the accuracy value is 93%, the precision value is 95%, and the recall value is 97%. In this research, a system model design is also carried out where the system can be integrated with the browser to open a user page on the classification of comments that have been input into the system.

HIDS and NIDS Hybrid Intrusion Detection System Model Design

With the popularity of Internet applications, network security has become one of the issues affecting the world economy. Currently, there is a large space to develop for intrusion detection systems as a relatively new field. For the faults of HIDS or NIDS network intrusion detection system, Papers has designed a hybrid HIDS and NIDS intrusion detection system model, and the introduction of Agent systems, finally through analysis the hybrid model of intrusion detection system, we can acquire its advantages.