Accepted by editor: 11-12-2020 | Final Revision: 21-12-2020 | Online Publication : 22-12-2020 1142 Accredited by National Journal Accreditation (ARJUNA) Managed by Ministry of Research, Technology, and Higher Education, Republic Indonesia with Second Grade (Peringkat 2, Sinta 2) since year 2017 to 2021 according to the decree No. 10/E/KPT/2019. Published online on the journal’s webpage: http://jurnal.iaii.or.id RESTI journal (System Engineering and Information Technology) Vol . 4 No. 6 (2020) 1142 – 1148 ISSN Electronic Media: 2580-0760 Sentiment Analysis for Detecting Cyberbullying Using TF-IDF and SVM Wahyu Adi Prabowo 1 , Fitriani Azizah 2 1,2 Department of Informatics Engineering , Faculty of Informatics, Telkom Institute of Technology Purwokerto 1 wahyuadi@ittelkom-pwt.ac.id, 2 16102051@ittelkom-pwt.ac.id Abstract 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 sentiment analysis stage, with the following steps are: 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. Keywords: Preprocessing, Term Frequency and Inverse Document Frequency, Support Vector Machine, Confusion Matrix, Application, Sentiment Analysis © 2020 RESTI Journal 1. Introduction For decades, the internet has been a part of life that can dynamically change the nature of a person such as children and adults [1], [2]. Internet is a type of network that connects information and communication globally. The internet is also an alternative way to obtain information sources directly [3]. The rapid growth of the social network has changed the meaning of friendship, relationships, and social communication. People have been interacting through social media such as Facebook, Twitter, Myspace, and YouTube that are accessed simultaneously [4]. From the rapid growth of social media, cyberbullying becomes one of the serious problems in social networks, especially for teenagers and adults [2]. Cyberbullying is defined as an aggressive and deliberate act to harm someone committed by a group or individual by using a form of electronic contact repeatedly or from time to time against a victim who cannot easily defend himself [5]. People have begun to realize that the incidence of cyberbullying has increased in recent decades, and some research shows that half of teenagers and society experience cyberbullying [6]. Even the effects of cyberbullying contribute to depressive stress, decreased self-esteem, despair, and suicidal desire among adolescents [7]. Social media is a medium to communicate its existence not only through media text but also users can use image and video media. It is from these materials that the media is widespread on the internet with the reach can quickly spread widely. With this capability there are many opportunities and opportunities from the internet shown, but there are concerns about increased online activity that could lead to the onset of deliberate crime and harassment such as cyberbullying. Social media apps are already very popular among everyone and the growing popularity of social media platforms is also increasing cyberbullying that occurs through social media [8], [9]. This cyberbullying phenomenon certainly gets special attention from the public and social media users, the role of information technology is a particular concern for researchers to develop technology to detect cases of cyberbullying. In detecting cyberbullying, the researcher can use the application of data and data mining concepts in finding text patterns, the process of analyzing text, and the process of summarizing useful information [10]. Even in research with the naïve Bayes method, the