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This study aims to analyze public sentiment toward the hashtag #KaburAjaDulu, which has circulated widely on the social media platform X (formerly Twitter). The hashtag reflects the growing anxiety among the public, especially younger generations, regarding socio-political issues in Indonesia. The data were collected using web scraping techniques, focusing on user-generated tweets that contain the hashtag. A comprehensive text preprocessing phase was conducted to clean the raw data by removing irrelevant elements such as URLs, emojis, numbers, and punctuation. The research applies a hybrid classification approach using a combination of Support Vector Machine (SVM) and Random Forest algorithms to categorize sentiment into three classes: positive, negative, and neutral. The performance of the model was evaluated using metrics such as accuracy, precision, recall, and F1-score to determine the effectiveness of the classification. The study aims to demonstrate that combining algorithms can improve classification performance compared to using a single algorithm. This research contributes to the field of sentiment analysis and provides valuable insights for researchers, policymakers, and social observers in understanding public opinion trends in digital media.

The development of social media, especially TWITTER, has become one of the main means for people to express opinions and criticism on various issues, including the performance of law in Indonesia. This study aims to analyze public sentiment towards the performance of law based on TWITTER user comments using the Naïve Bayes algorithm. The research data consists of 1004 comments collected from several videos related to legal topics. The analysis process includes the stages of data crawling, pre- processing (text cleaning, normalization, and tokenization), labeling sentiment into positive, negative, and neutral, and testing the Naïve Bayes model. The results show that the Naïve Bayes algorithm is able to classify sentiment with an accuracy level of 93.73%. The distribution of sentiment from 1004 comments shows that the majority of public opinion is (negative/positive/neutral), which indicates that public perception of the performance of law is still (critical/positive). These findings are expected to be input for related parties to understand public opinion and improve the quality of legal performance in

Social media X (Twitter) has become the main platform for the Indonesian public to express opinions, including on the trend of 'kabur aja dulu' (let's just run away for a bit). This research aims to classify the sentiments of the public using the Naïve Bayes and Support Vector Machine (SVM) methods, and to compare the accuracy of both in sentiment analysis. Data was collected via the Twitter API with the hashtag #kaburajadulu, resulting in 2,067 tweets, which, after the cleansing process and manual labeling, left 385 data points. The analysis process followed the CRISP-DM stages, which include business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Model evaluation was conducted using a confusion matrix with accuracy, precision, and recall metrics. The classification results show that 82% of tweets have a positive sentiment and 18% negative. The Naïve Bayes algorithm achieved an accuracy of 86.49%, slightly lower than SVM, which reached 88.05%. In conclusion, Support Vector Machine is more effective in sentiment classification on public opinion data. This research contributes to the digital mapping of public opinion and recommends the development of automatic labeling methods as well as the exploration of advanced algorithms in the future.

QRIS (Quick Response Code Indonesian Standard) has become a widely used national digital payment standard. User satisfaction with this service needs to be monitored continuously to ensure its sustainability. This study aims to predict the level of QRIS user satisfaction based on their experiences and perceptions expressed organically on the Twitter social media platform. The method used is sentiment analysis with the Naive Bayes classification algorithm implemented using RapidMiner software. The research data was obtained from Twitter user comments collected through web scraping techniques. The text data then went through a preprocessing stage that included cleansing, stopword filtering, stemming, and tokenizing to be prepared as features ready to be processed by the model. The data was divided into training (80%) and testing (20%) subsets for model training and validation. The results showed that the Naive Bayes model was able to predict user satisfaction sentiment with an accuracy of 80.99%. These findings indicate that the model is highly accurate in identifying satisfied comments and sufficiently sensitive in detecting dissatisfaction. This study concludes that sentiment analysis of Twitter UGC data using Naive Bayes is an effective and efficient approach for predicting QRIS user satisfaction in real time. The practical implication of this study is to provide an automatic feedback system for service providers to monitor public sentiment and take targeted corrective actions.

The rise of social media has created a digital public sphere that enables users to express their opinions on social and political issues openly and in real-time. One of the most discussed topics on social media platform X is the trending hashtag #IndonesiaGelap, which reflects public concern and criticism regarding various governmental and societal conditions. This study aims to conduct sentiment analysis on tweets containing the hashtag to determine the overall sentiment trend among users. The method employed in this research is the Naive Bayes classification algorithm, known for its simplicity and effectiveness in text classification. To enhance the model’s performance, Particle Swarm Optimization (PSO) is applied to optimize feature selection and parameter tuning. The dataset consists of public tweets collected via the Twitter API, followed by preprocessing, feature extraction using TF-IDF, and sentiment classification into three categories: positive, negative, and neutral. The results indicate that the integration of PSO significantly improves the classification accuracy of the Naive Bayes model compared to the baseline. The majority of tweets related to #IndonesiaGelap exhibit a negative sentiment, indicating widespread public dissatisfaction and criticism. This research is expected to contribute to a better understanding of public perception and serve as valuable input for stakeholders in addressing social issues in the digital age.

The rapid development of social media, particularly Twitter, has given rise to a new form of social violence known as cyberbullying. This study aims to explore the phenomenon of cyberbullying on Twitter using Ulrich Beck's Risk Society Theory as an analytical framework. The research approach used is a literature review. This study perceives cyberbullying on Twitter as a modern, systemic risk, shaped by anonymity, cancel culture, and the individualization of risk. It acknowledges that Twitter's structural features, such as pseudonymous accounts and the rapid dissemination of information, exacerbate the potential for cyberbullying, while simultaneously positioning individual users as both victims and potential perpetrators of digital violence. These findings reinforce Beck's thesis that risks in advanced modernity are self-produced, institutionally distributed, and difficult to regulate, clearly reflected in the uncontrolled spread of cyberbullying in digital public spaces.

The development of digital technology has significantly changed the way students interact and present themselves in social life, especially through social media such as Instagram, TikTok, and Twitter. One of the emerging social phenomena is flexing, which refers to the behavior of showing off lifestyle, achievements, or ownership to gain attention and social recognition. This study aims to analyze how Sociology students of Sriwijaya University interpret flexing on social media and whether flexing is more dominant as a lifestyle or as a form of social pressure. This research uses a qualitative method with a literature review approach by examining various scientific articles, journals, and previous studies related to flexing, self-presentation, symbolic consumption, social validation, and Fear of Missing Out (FoMO). The results show that flexing is not only a form of self-presentation and symbolic consumption, but also a response to social pressure in the digital environment.

The development of technology in the era of globalization has brought significant changes in society, particularly through the emergence of the internet and social media such as WhatsApp, X (Twitter), Facebook, Instagram, Telegram, and TikTok, which facilitate rapid information dissemination. This development has also given rise to a new profession, namely content creators, who produce and share content in the form of images, videos, or text for branding, professional purposes, or self-expression, often resorting to sensationalism to attract audience attention. On the other hand, the ease of access to social media has also triggered the spread of negative content, including pornography, as evidenced by Komdigi/Kominfo data showing millions of blocked negative content, with X being one of the dominant platforms. In Islamic perspective, anything that leads to adultery is prohibited as stated in QS. Al-Isra verse 32. A prominent case is Dea OnlyFans (Gusti Ayu Dewanti) who was arrested for distributing pornographic content through OnlyFans and Google Drive, charged under the Pornography Law and ITE Law, and found guilty in the Supreme Court Decision Number 2086 K/Pid.Sus/2023. This study discusses 1) How are the differences in judges' considerations at the District Court, High Court, and Cassation? 2) Can the Supreme Court judges' considerations provide a deterrent effect? This research uses a descriptive method with normative legal research based on literature study, using primary, secondary, and tertiary legal materials.

This study examines the phenomenon of K-Pop slang usage among young generations and its influence on lifestyle and social identity formation. The research background reveals that K-Pop culture has significantly impacted language practices, particularly through the adoption of specific terms within fan communities. The objective is to analyze how K-Pop slang functions as a linguistic creativity tool and identity marker among Indonesian youth. Using a qualitative descriptive approach, data were collected from social media platform X (Twitter) through purposive sampling of posts containing K-Pop slang terms. Findings indicate that K-Pop slang encompasses semantic shifts, acronyms, abbreviations, and new terminology that strengthen community solidarity and collective identity. The implications suggest that while K-Pop slang serves as creative expression and social bonding mechanism, excessive usage may affect proper Indonesian language proficiency in academic contexts. This research contributes to understanding the balance between linguistic creativity, global cultural influence, and Indonesian language preservation.