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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.

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 popularity of the boys love (BL) genre, a key representation of the LGBTQ community, raises concerns about its potential negative impact on social pathology related to LGBTQ behaviors among fans. Despite the stigma labeling BL enthusiasts as exhibiting deviant sexual behavior (homosexuality), early observations and field findings show that most fans are heterosexual and do not exhibit such behavior. Instead, their engagement represents a cumulative effort to support LGBTQ representation. This research uses a qualitative case study approach, conducted on social media X over four months. Key findings suggest that the primary curative efforts by fans to address the social pathology of LGBTQ are self-control and the establishment of self-boundaries. Self-control is demonstrated through cognitive and decision-making control, while self-boundaries are seen in fans' understanding that fictional content should not be translated into heteronormative reality. Strengthening self-control and self-boundaries lays the foundation for further curative actions, preventing fans from becoming fully integrated into the LGBTQ community.

Social media has become a new communication space that allows for the rapid and widespread dissemination of information, including on public issues. One of the platforms that is often used is social media X (formerly Twitter), where users can express their opinions and shape public perception. This study is entitled "The Influence of Hate Speech by Admin @keretacepatid on Patrick Kluivert (National Team Coach) on the Level of Followers' Perception on Social Media X" which aims to determine how hatred influences the formation of perceptions of social media users. This study uses a quantitative approach with a survey method and data collection techniques through online questionnaires to followers of the @keretacepatid account. The results of the study indicate that there is a significant influence of hatred on followers' perceptions. Some respondents showed a dominant attitude, namely agreeing with the negative narrative built by the account against Patrick Kluivert, while others were in a negotiating position, namely understanding the context of the speech but still considering Kluivert's professional background as a national team coach. This finding is the importance of ethical and responsible digital communication management in shaping public opinion in the era of social media.

This study aims to determine the forms of interaction, participation, and motivation of Indonesian NCTzens in the boycott campaign of pro-Israel products through the @nctzenhumanity account on social media X. This study uses Henry Jenkins' theory of participatory culture and the concept of digital activism as the basis of analysis. The data for this study were obtained through semi-structured interviews with informants directly involved in the campaign, observations of campaign content, and digital documentation from the @nctzenhumanity account and related articles. Data validation techniques were carried out by triangulating sources to ensure the accuracy and consistency of the information obtained. The results showed that NCTzen interactions in the boycott campaign occurred through various activities, such as discussions, providing positive affirmations, and exchanging information that formed a virtual community. Their participation included digital actions such as retweets, raising hashtags, content creation, and sharing information that raised awareness about the boycott issue. In addition, real support was also given in the form of donations and boycotts of products originating from pro-Israel companies. Motivation for participation was driven by educational, moral, spiritual, emotional values, and fandom solidarity towards humanitarian issues. This campaign reflects the practice of participatory culture in fandom digital activism, where fandom transforms into an active social actor in the struggle for humanitarian issues through digital media.

The free lunch program is a goverment initiative aimed at addressing the issue of stunting in Indonesia. This program focuses on toddlers, school-age children and pregnant women. Various opinions have emerged from the public regarding this initiative, especially through sosial media platform X (Twitter) and news portals. In this research, sentiment analysis was conducted to understand public responses to the program, whether they are positive, neutral or negative. To evaluate the accuracy of the sentiment analysis perfomed, a deep learning approach was applied using the Long Short-Term Memory (LSTM) algorithm. The results show that public sentiment varies responses, on social media X tend to be negative, while those on news portals tend to be positive toward the free lunch program in Indonesia. Through LSTM-based testing, sentiment analysis on tweet data achieved an accuracy of 88.6%, with a precision of 84.6%, recall of 88.6% and an F1-Score of 86.3%. Meanwhile, sentiment analysis on news portal data reached an accuracy of 89%, with a precision of 81.7%, recall of 89% and an F1-Score of 85.1%.

Among social media users x is one of the popular platforms. The x application plays a significant role in dissemination and development that not only becomes a medium for spreading news but also becomes an ecosystem that supports evolution and adaptation. Through interaction and creativity contribute to dynamics, making application one of the major factors in development in the digital age. In this article, the writer reviewed social media users x. this study aims to identify the types of times in which they often use and to understand their context. Through qualitative approaches data is collected from the posts of social media users x and analyzed using the method of analyse analysis. Results. Research shows that social media USES a lot of information about other news items. Much about applications x:slang, viral news, online loan services (pinjol), and so forth, takes serious action from governments, communities, and parents to address this problem. Social media plays a very important role in the digital age, referring to the use of social media platforms to interact and communicate with others. There are positive effects on social media x (facilitating interaction and expanding association, there are many viral things), but it can also have a negative effect like spreading false information.

Rohingya in Indonesia has become trending conversation on social media. Sentiment analysis can get public responds. Big data makes the problem time efficiency labeling process, therefore the lexicon dictionary is needed for the labeling process. Data is growing and circulating very rapidly so it takes a fast and efficient time. Although it is fast and makes it easier to solve problems, it is still necessary to question the accuracy produced when using the lexicon labeling. A comparison of the labeling process between the InSet lexicon and the VADER lexicon was conducted to determine the accuracy of the labeling. It was done by combining lexicon with machine learning method of support vector machine and TF-IDF weighting and accuracy result calculated using confusion marix. Data from social media X as many as 9117 lines and labeled with InSet lexicon result 5241 negative sentiments, 1369 positive, and 521 neutral. Then the labeling results with VADER produced 2749 positive, 2523 negative, and 1881 neutral. After labeled, processed SVM and calculated accuracy with results of InSet lexicon accuracy having an average of 85.8% while the VADER SVM lexicon has an average of 82.65%.  

Globalization brings many influences and changes in human life. The rapid dissemination of information obtained through social media can cause change. X account @discountfess as a forum that favolitates the dissemination of information quickly and widely can influence a person's behavior, one of wich is consumprive behavior. This study aims to describe the consumptive behavior of folllwers account @discountfess at social media X, using a descriptive method of quantitative approach. Sampling was accidental sampling with 96 followers @discountfess account. Data collection techniques include observation, questionnaire, documentation and literature study. The results show that the consumptive behavior of followers account @discountfess that causes impulsive purchases in the moderate category of 57%, irrational purchase in the moderate category with a percentage 60% and waste in the high category with a percentage 52%.

The Free Nutritious Meals (MBG) program launched by the Indonesian government aims to address the problem of malnutrition in children and students. However, the acceptance of this program in the community still requires in-depth evaluation because there are many negative sentiments that dominate on social media. This study aims to analyze the sentiment of the Indonesian community regarding the Free Nutritious Meals program on social media X (Twitter) using the Bidirectional Gated Recurrent Unit (BiGRU) model. Of the 1,405 tweet data obtained, 57% were negative opinions and 43% were positive opinions. The evaluation results show that the BiGRU model with FastText support to handle potential overfitting, is able to classify sentiment effectively, with an accuracy of 80%. Sentiment analysis shows that the majority of public responses to the Free Nutritious Meals (MBG) program tend to be negative, with 798 negative tweets and 607 positive. This reflects public dissatisfaction with the implementation of the program and highlights the need for evaluation and improvements so that the benefits can be more widely felt by the community.