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This study examines the emergence of localized English expressions in Indonesian social media discourse, highlighting how English is adapted and reinterpreted in multilingual digital contexts. While previous studies on English in Indonesia have primarily focused on formal domains such as education and language policy, limited attention has been given to informal online communication where linguistic innovation is highly visible. This study aims to analyze how English lexical items are re-semanticized and creatively transformed by Indonesian users on social media platforms. The study employed a qualitative approach using data collected from Instagram posts and comments, focusing on selected examples of English-derived expressions such as boba, gas, voucher, server, and suamiable. The analysis compares the conventional meanings of these terms in Inner Circle English with their localized meanings in Indonesian usage. The findings reveal that English functions as a flexible linguistic resource, undergoing processes of semantic shift, metaphorical extension, and lexical blending. These localized forms reflect users’ creativity as well as their ability to adapt global linguistic resources to local cultural contexts. The study concludes that English in Indonesian social media is not merely borrowed but actively reconstructed, contributing to the dynamic development of English in the Expanding Circle.

This study examines the phenomena of code-switching and code-mixing in the digital interactions of Indonesian Generation Z on Instagram. Using a sociolinguistic approach with virtual ethnography, data were collected from 1,200 posts and comments published between January and June 2024, complemented by in-depth interviews to explore the factors influencing language choice. The findings reveal that code-switching occurred in 68.4% of the data, with intrasentential switching as the dominant pattern (47.3%), followed by intersentential switching (38.6%) and external switching (14.1%), indicating Generation Z’s high multilingual competence. Code-mixing appeared in 82.1% of the data, primarily through the insertion of English vocabulary into Indonesian (63.2%), followed by regional languages such as Javanese, Sundanese, and Betawi (27.1%), particularly in nostalgic, culinary, and emotionally expressive content. The main factors influencing these practices include social identity, community affiliation, communicative efficiency, emotional expression, and audience context. The study concludes that code-switching and code-mixing function as deliberate communicative strategies that reflect Generation Z’s hybrid identity in digital spaces, offering important implications for digital sociolinguistics, language education, language policy, and digital content development.

The rapid growth of TikTok has transformed digital communication practices and created new opportunities for content creators to establish stronger relationships with their audiences. From the perspective of Digital Public Relations, communication style plays a crucial role in influencing follower engagement and enhancing the effectiveness of online interactions. This study aims to examine the communication styles employed by TikTok content creators to build and strengthen follower engagement. The research adopts a qualitative approach using the Systematic Literature Review (SLR) method by analyzing relevant scholarly articles published between 2021 and 2026. The findings reveal that communication styles emphasizing authenticity, interactivity, storytelling, content consistency, and emotional connection significantly contribute to higher audience engagement, as reflected in the number of likes, comments, shares, and active participation. Furthermore, the effective use of trending content, TikTok's algorithmic features, and adaptive communication strategies strengthens relationships between content creators and followers while enhancing credibility and digital presence. The review also identifies opportunities for future research on the influence of audience characteristics, digital culture, and evolving social media algorithms on the development of sustainable engagement in digital communication.

The Free Nutritional Meal Program (MBG) is a government policy that is widely discussed by the public through social media, especially TikTok. Various comments that have emerged indicate differences in public opinion towards the program, so an analysis is needed to determine the tendency of public sentiment. This study aims to analyze TikTok user sentiment towards the Free Nutritional Meal Program using the Naive Bayes method. The research method is carried out through several steps, namely collecting TikTok comment data, preprocessing text, labeling sentiment data into positive, negative, and neutral, feature transformation using TF-IDF, and classification using the Naive Bayes algorithm. Based on the analysis of 500 comment data, the results show that positive sentiment dominates public opinion by 42% (210 data), followed by negative sentiment by 36% (180 data), and neutral sentiment by 22% (110 data). Testing the classification model using Naive Bayes produces excellent performance with an accuracy rate of 86%, precision of 84%, recall of 85%, and F1-score of 84%. The conclusion of this study shows that the Naive Bayes method is effective as an approach in social media sentiment analysis to map public responses to government policies.

This study analyzes netizen sentiment concerning the 17+8 public aspirations circulating the digital platform X spanning the period from August 18 through October 31, 2025. 1,837 comments obtained through scraping method. Classification Research stages include data preprocessing, sentiment weighting based on lexicon, and feature extraction using TF-IDF. Data 80% used for learning purposes and the remaining 20% utilized for validation. The findings reveal that the majority of comments, amounting to 81.14%, contained negative sentiment, while the remaining 18.86% were positive. The outcomes demonstrate that community reactions toward the 17+8 People's Demands were dominated by unsupportive views. From a theoretical standpoint this scholarly work offers to enriching knowledge concerning public opinion classification on political issues through a computational approach, while also serving as a reference for future research focused on improving the accuracy of sentiment analysis related to political dynamics and the behavior of state institutions.

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 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

This study aims to develop a sentiment classification method for YouTube user comments related to the game Love and Deepspace using the Naïve Bayes algorithm, focusing on improving the text data processing and understanding user perceptions. Comment data were collected through scraping from YouTube videos, followed by preprocessing including text cleaning, normalization, stopword removal, stemming, and translation into English. Initial labeling was conducted using TextBlob, then the data were randomly sampled for training the Naïve Bayes model. Evaluation involved comparing sentiment distributions and visualization using Word Cloud and bar charts. The Naïve Bayes model achieved an accuracy of 77.36% in sentiment classification. The sentiment distribution shows differences between TextBlob (positive: 1,011, neutral: 1,312, negative: 575) and Naïve Bayes (positive: 901, neutral: 1,627, negative: 370), with Naïve Bayes being more conservative. The Word Cloud visualization identifies dominant words such as "bang," "game," and "main," while the bar chart shows the largest proportion of neutral sentiment. Naïve Bayes is effective for sentiment classification on informal comment data, with significant differences from rule-based methods like TextBlob. This research contributes to the development of text data processing techniques and user perception analysis, as well as opening up optimization opportunities with other algorithms like SVM for better accuracy.

This research examines digital charity practices within Christian media communication on YouTube, focusing on two distinct donation formats: marapthon live stream donations (e.g., 24‑hour fundraising events) and sermon‑based donations (offerings collected during or after online worship services). Despite the rapid growth of faith‑based online giving, a critical problem remains: the absence of an integrated system that aligns these two donation models with Christian values of transparency, accountability, and community stewardship. Existing platforms often treat live marapthon and sermon donations separately, leading to fragmented donor experiences and inefficient fund utilization. Therefore, this study aims to develop a conceptual framework for an integrated digital charity system by comparatively analyzing media communication strategies in both donation contexts. The proposed method is a netnographic comparative analysis, involving systematic observation of YouTube comments, chat logs, and video descriptions from 10 Christian channels (5 marapthon‑focused, 5 sermon‑focused) over six months, supplemented by semi‑structured interviews with content creators and donors. The main findings reveal that marapthon donations emphasize urgency and real‑time social proof, while sermon donations rely on theological framing and pastoral trust. The synthesis proposes a hybrid system architecture incorporating real‑time donation tracking, automated acknowledgment, and weekly theological reflection modules. In conclusion, integrating both models into a single development framework enhances donor engagement and aligns digital charity with Christian communication ethics, offering practical guidelines for church‑based YouTubers and platform developers.

Social media has evolved into a significant platform where consumers freely express their opinions, experiences, and levels of satisfaction regarding various products, including those offered by Micro, Small, and Medium Enterprises (MSMEs). The comments and reviews shared by customers on these platforms contain diverse sentiments that can serve as valuable indicators of how consumers perceive product quality. Understanding these sentiments is crucial for MSME owners, as it allows them to evaluate their products and adapt to market expectations more effectively. This study aims to analyze customer sentiment toward MSME products on social media by utilizing the Naïve Bayes algorithm, a widely used classification method in text mining. The data used in this research consist of customer comments collected from various social media platforms. The research process involves several stages, including data collection, manual labeling of sentiments, text preprocessing (such as tokenization, case folding, and stopword removal), and splitting the dataset into training and testing subsets. Subsequently, the classification process is carried out using the Naïve Bayes algorithm to categorize sentiments into positive, negative, and neutral classes. The results of this study demonstrate that the Naïve Bayes method is effective in classifying customer sentiments with a satisfactory level of accuracy. These findings provide a comprehensive overview of consumer perceptions regarding the quality of MSME products. Furthermore, this research is expected to assist MSME business owners in understanding customer feedback more systematically and using it as a basis for improving product quality and enhancing customer satisfaction in a competitive digital marketplace.

This study examines the phenomenon of cyberbullying against people with disabilities in the comment column of the TikTok platform through a forensic linguistic perspective. The focus of the research is directed at the form of speech used by the perpetrator in carrying out verbal attacks. The research method uses a qualitative descriptive approach based on John Searle's speech action theory. Data was collected from comments that contained indications of cyberbullying and analyzed based on speech categories. The results showed that cyberbullying was dominated by assertive (22 findings) and expressive (14 data) speech, with the category of insults as the main instrument of verbal attack. The dominance of assertive speech and expressive insults proves that the perpetrator consciously uses statements to demean people with disabilities, either through derogatory jokes or with explicit intent to insult. These findings confirm that language in the digital space not only serves as a means of communication, but can also be an instrument of symbolic violence that impacts the dignity of the individual. This research is expected to contribute to the development of forensic linguistic studies and become the basis for efforts to prevent and handle cyberbullying against vulnerable groups on social media.

Advances in digital technology have transformed the paradigm of faith proclamation, requiring the Church to optimize social media as a relevant catechetical instrument. This study aims to examine how the use of the TikTok platform can be optimized as an effective means of creative catechesis in connecting complex faith teachings for people in the digital era. The research method used is descriptive qualitative with a literature study technique, where data is collected from various scientific literature, Church documents, and relevant library sources. The research findings indicate that TikTok is an effective digital space for catechesis because it is supported by attractive audio-visual features, interactive features such as stitches and comments that enable two-way dialogue, and an algorithmic system that expands the reach of proclamation. The strategy of catechesis through short videos has proven to be able to change the perception of faith teaching that has become rigid to a more personal spiritual experience that is easily understood by all levels of society, especially the younger generation. The implications of this study emphasize the need for the Church to consistently adapt to digital culture and increase content creativity to ensure the continuity of inclusive evangelization amidst the dynamics of modern developments.

Speaking skills are regarded as one of the most difficult skills to be acquired by EFL students, especially high school EFL students, because of language and psychological factors. This paper attempts to examine high school EFL students’ speaking difficulties as identified by peer feedback in TikTok speaking activities. Qualitative descriptive design was adopted in this study by involving 15 tenth-grade students in carrying out speaking activities in the form of recording TikTok video clips and commenting on their peers’ videos. The data obtained include speaking clips, peer feedback comments, and reflective statements of the participants, which were analyzed using a thematic analysis approach. The results found that there were four different types of speaking difficulties, namely fluency difficulties, pronunciation difficulties, organizational difficulties, and difficulties related to confidence and anxiety levels of students when conducting a speaking activity. This can be evidenced by the students’ hesitation, frequent pauses, mispronunciation, disorganized speaking, and lack of confidence when speaking. Peer feedback comments play an important role in discovering this problem pattern among high school EFL students.

Indonesian culinary comments on social media platforms such as Instagram are characterized by informal spelling, regional language mixing, slang expressions, and emojis, posing substantial challenges for automated sentiment classification. While IndoBERT has demonstrated strong performance across Indonesian natural language processing tasks, the contribution of individual preprocessing components to fine-tuning performance on informal text remains underexplored, particularly in the culinary domain. This study addresses this gap by conducting a systematic preprocessing ablation study on IndoBERT-Base fine-tuning for Indonesian culinary sentiment classification, accompanied by a comparative evaluation against Naive Bayes with TF-IDF, SVM with TF-IDF, and BiLSTM as representative baselines. A dataset of 3,500 manually labeled Instagram culinary comments across three sentiment classes was used, with a stratified 80/10/10 split. Six preprocessing variants were evaluated under identical experimental conditions to isolate the contribution of each component. The results show that slang normalization is the most impactful single preprocessing step, yielding a macro F1-score gain of +0.0609 over the no-preprocessing baseline, while the full pipeline achieves an accuracy of 0.8800 and a macro F1-score of 0.8465. IndoBERT-Base with the full pipeline outperforms all baselines across all evaluation metrics. Per-class analysis reveals that the negative class achieves the lowest F1-score of 0.7600, with sarcastic expressions and Banjar regional vocabulary identified as primary sources of misclassification. These findings indicate that preprocessing decisions have a measurable and non-uniform effect on IndoBERT fine-tuning performance. In this study, slang normalization provides the most substantial individual contribution in bridging the vocabulary gap between informal user-generated text and the model’s pre-training distribution.

This study examines the use of politeness principles in netizens’ comments on the Instagram account of Prabowo Subianto. The focus of the research is on utterances that reflect the six maxims of politeness proposed by Leech, namely the tact maxim, generosity maxim, approbation maxim, modesty maxim, agreement maxim, and sympathy maxim. This research employed a qualitative method with a content analysis approach. The data were collected through digital documentation, listening, and note-taking techniques from netizens’ comments on Prabowo Subianto’s Instagram post dated April 13, 2026. The findings show that five politeness maxims were identified in the data, namely the tact maxim with 78 utterances, the approbation maxim with 145 utterances, the modesty maxim with 4 utterances, the agreement maxim with 190 utterances, and the sympathy maxim with 33 utterances. Meanwhile, the generosity maxim was not found in the data. These findings indicate that the comments on the post generally express polite, appreciative, and supportive attitudes toward Prabowo Subianto.

This study examines the application of Walter Murch's Rule of Six as a post-production decision-making framework for the Instagram Reels content on the Tenchi Tirtayasa account. This project addresses the challenge of bridging the gap between the image of a premium restaurant and the necessity to convey a warmer, more accessible brand experience through short-form videos. A descriptive project-based methodology was employed, involving the observation of the production process, documentation of the editing workflow, analysis of Episode 3 of the Reels series, and review of Instagram Reels Insights as the data. The analysis reveals that the six priorities of editing—Emotion, Story, Rhythm, eye-trace, Two-dimensional screen space, and Three-dimensional action space—assisted the editor in selecting shots, organizing visual sequences, managing pacing, and maintaining spatial continuity in the vertical 9:16 format. Episode 3 garnered 9,132 Views, 229 Likes, 10 Comments, 9 Shares, and a 93.4% non-follower reach rate. These findings suggest that a Rule of Six-based workflow can enhance soft-selling Storytelling, reinforce visual consistency, and improve the communicative value of restaurant reel content.

The development of digital technology provides new opportunities for learning innovation, including in literature learning at Sma Negeri 1 Serang City. This study aims to describe the use of Blogger as a literature learning medium in improving short story writing skills of high school students in the digital era. This study uses a qualitative approach with descriptive methods. The subjects of the study were students of SMA Negeri 1 Serang City with a population of approximately 1,756 students who participated in short story writing learning based on Blogger as a publication and learning medium. Data collection techniques were carried out through observation, interviews, and document analysis of the results of students' short stories published on Blogger. The purpose of this study is to determine the benefits of using Blogger in literature learning to increase student learning motivation, provide space for creativity in writing, and make it easier for students to publish and share their literary works digitally. In addition, Blogger media also encourages students to be more active in the learning process because of interactions through comments and feedback from teachers and peers. Thus, the use of Blogger as a literature learning medium can be an innovative alternative in improving short story writing skills of high school students in the digital era.

This study aims to analyze the effect of product bundling strategies, countdown timer urgency, and social proof on the interactive responses of Generation Alpha consumers toward Scora skincare products in Bandar Lampung, with live streaming as a moderating variable. Interactive responses in this study include comments, questions, likes, and purchase actions during live streaming sessions. This research employs a quantitative approach, with data collected through ques-tionnaires. The sample consists of 300 Generation Alpha respondents in Bandar Lampung who have watched Scora’s live streaming promotions. Data analysis techniques include multiple linear regression analysis and Moderated Regression Analysis (MRA). The results indicate that product bundling strategies, countdown timer urgency, and social proof have a positive and significant effect on interactive responses, both partially and simultaneously. However, the moderation test results reveal that live streaming does not moderate the relationship between product bundling strategies, countdown timer urgency, and social proof on interactive responses. These findings suggest that although live streaming serves as a primary promotional medium, it does not necessarily strengthen the influence of marketing strategies on Generation Alpha’s in-teractive responses. This study is expected to provide practical insights for local skincare brands in developing more effective digital marketing strategies through live streaming commerce.

The proliferation of online gambling promotional comments on Indonesian social media has become a serious issue requiring fast and accurate automated handling. This study aims to implement a Hybrid Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) method to classify online gambling comments and compare its performance with standalone RNN and LSTM models. The research utilized a dataset of 10,230 comments subjected to comprehensive preprocessing stages, including the normalization of non-standard language using a slang dictionary. Testing was conducted across three data-splitting scenarios: 90:10, 80:20, and 70:30. Experimental results demonstrate that the standalone LSTM model achieved the highest average accuracy of 97.45%. However, the Hybrid RNN–LSTM model showed significant superiority in terms of performance stability, yielding the lowest standard deviation (0.0027) and the smallest Coefficient of Variation (0.28%) across all scenarios. These findings indicate that while the LSTM architecture is highly effective at capturing short-text context, the Hybrid approach provides better robustness against fluctuations in data proportions, making it highly relevant for implementation as an automated detection system on social media.

The phenomenon of digital da'wah has significantly transformed the ways religious knowledge is accessed, interpreted, and disseminated in contemporary society. This study aims to analyze the patterns of actions and perceptions of TikTok and YouTube users toward religious content delivered by Gus Baha within the context of the digital religious space. Using Max Weber’s theory of rationalization as an analytical framework, this research explores how religious authority, knowledge transmission, and user interpretation undergo processes of rationalization on digital platforms.This study employs a qualitative approach using virtual ethnography, content analysis, and in-depth interviews with users of both platforms. Data were collected through observation of uploaded content, analysis of user interactions and comments, and examination of engagement dynamics.The findings indicate that user responses to Gus Baha’s content reflect Weber’s four types of social action. Instrumentally rational actions are evident in the use of short videos as practical and efficient learning tools. Value-oriented rational actions appear in users’ consistent efforts to deepen religious understanding. Affective actions emerge from emotional attachment to Gus Baha’s communicative style, while traditional actions are reflected in the perception of digital da'wah as a continuation of established religious learning traditions. Furthermore, digital rationalization through algorithms, short-video formats, and platform accessibility, shapes how religious knowledge is selected, interpreted, and circulated.This study concludes that digital religious spaces function not only as channels of dissemination but also as arenas for the transformation of religious authority, meaning construction, and religious practice in the digital era.