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This study explores Indonesian EFL student teachers’ experiences of learning to write news articles through blogging in a Journalism course. The study was motivated by the need to provide meaningful and genre-based writing practice for EFL student teachers, who often face difficulties in generating ideas, organizing information, meeting genre expectations, and writing for authentic aences. Employing a qualitative case study design, the study involved twenty undergraduate student teachers from a private Islamic university in Tangerang, Indonesia; all participants completed an online quetaire, and five volunteers joined follow-up semi-structured interviews. The questionnaire data were alysed descriptively, while the interview data were examined thematically. The findings indicate that blogging supported regular writing practice, helped students understand news text structure, enouraged clearer organization of headlines, leads, and supporting details, and increased engagement by allowing students to publish and share their work. Blogging also promoted peer interaction and selfevaluation because students could read, compare, and comment on one another’s posts. However, some paticpants eprienced challenges related to technical blogging skills, uncertainty about writing standards, limited peer feedback, and discomfort with public online publication. The study concludes that blogging can serve as a useful platform for EFL news writing when it is supported by explicit istrucion, relevant tasks, clear assessment criteria, structured feedback, and ethical awareness in using digital and AI-driven tools.

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.

The presence of Generation Z, who grew up entirely in the digital era, has triggered a fundamental transformation in Muslim religious practices, where social media has replaced conventional religious institutions as the primary source of religious information. This condition creates a paradox: Gen Z has become a generation that is highly religious online, yet is often disconnected from physical communities and traditional religious authorities. This study aims to analyze the character of Muslim Gen Z religiosity, identify its forming factors, and examine the impact of the digital era on their religiosity. This study employed a descriptive qualitative approach using a library research method. The findings indicate that Muslim Gen Z religiosity is characterized by personalization, flexibility, and digital spirituality, strongly influenced by social media. These characteristics are shaped by massive digital technology accessibility, the effectiveness of contextual Islamic preaching on platforms such as TikTok, as well as spiritual needs and social pressure from the digital environment. The digital era brings positive impacts in the form of increased accessibility and religious literacy, but also negative impacts including shallow religious understanding, vulnerability to information bias, and potential exposure to extreme ideologies. This study implies the importance of an integrated digital religious literacy strategy through critical thinking-based Islamic Religious Education curriculum reform, enhancement of educators' digital capacity, and cross-sector collaboration to strengthen Gen Z's moderate and reflective religious understanding.

This article examines the re-actualization of teachers’ social competence within the digital learning ecosystem. The rapid development of technology has transformed conventional learning into digital-based environments, which significantly affect the patterns of interaction between teachers and students. This transformation often leads to reduced direct communication, emotional connection, and social engagement in the learning process. Therefore, this study aims to explore the importance of strengthening teachers’ social competence and to identify strategies for its re-actualization in digital learning contexts, particularly in early childhood education. This study uses a qualitative approach through a literature review by analyzing relevant journals, books, and scientific articles published in recent years. The findings indicate that teachers’ social competence remains a crucial factor in creating meaningful learning, even in virtual environments. Effective communication, empathy, and interactive engagement must be adapted to digital platforms. The study also highlights several strategies, such as integrating humanistic approaches, optimizing interactive media, and enhancing digital communication skills. These findings imply that strengthening teachers’ social competence is essential to support effective and holistic learning in the digital era.

This research is motivated by the problems in manual attendance systems at schools, which remain vulnerable to fraud, time-consuming, and inefficient. The expected solution is to develop an automated attendance system based on face recognition that can operate in realtime with high accuracy. The research object is vocational high school students, with the applied method implementing the YOLO v10 algorithm for face detection, followed by the face_recognition library for identification. The instruments used include an Imou CCTV camera as the input device, a mid-range laptop as the hardware platform, and Python with SQLite as the software environment for data processing and attendance storage. The results show that the developed system achieved an average face detection accuracy of 96% under normal lighting and 91% under low lighting, with an average processing speed of 27 FPS. The implementation of an anti-duplication feature also ensured data validity by allowing each student to be recorded only once per day. In conclusion, the use of YOLO v10 in face-based attendance proved to be effective, efficient, and capable of reducing fraud. The implication of this study is that the system can be applied in both Islamic boarding schools and general schools as a modernization of attendance systems, with a recommendation for further development through web-based application and cloud database integration.

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.

Game collaboration branding has emerged as a significant marketing strategy influencing player purchasing behavior. This study aims to analyze the influence of collaboration branding, player satisfaction, and emotional attachment on purchase intention among Honkai: Star Rail players. 280 active members of the Pompomfess community on Platform X (Twitter) participated in a quantitative survey that was analyzed using Structural Equation Modeling–Partial Least Squares (SEMPLS). The results extend the application of the Theory of Planned Behavior to digital game consumption behavior by showing that collaboration branding, player satisfaction, and emotional attachment significantly increases purchase intention. In addition to offering useful advice for game developers and marketers in creating player-oriented brand collaboration strategies, this study conceptually advances the body of knowledge on digital consumer behavior.

The growth of information technology has driven changes in consumer behavior, one of which is through e-commerce platforms such as Shopee. This phenomenon has generated a large number of customer reviews, including those for local cosmetic products such as Wardah. These reviews serve as an important source of information for understanding customer perceptions and satisfaction levels. However, manual analysis of large and linguistically diverse datasets is inefficient and potentially subjective. This study aims to implement the multi-category Naive Bayes algorithm to classify the sentiment of Wardah product reviews on Shopee into three categories: positive, negative, and neutral. The data were collected using a web scraping technique and processed through a series of preprocessing stages including case folding, tokenization, stopword removal, stemming, and text cleaning. Subsequently, term weighting was performed using the TF-IDF method prior to classification. Model performance was evaluated using a confusion matrix as well as accuracy, precision, and recall metrics. The results indicate that the multi-category Naive Bayes algorithm achieved an accuracy of 86.00%, a precision of 86.63%, and a recall of 98.24%. This approach can assist business practitioners in objectively understanding customer opinions and support decision-making in business strategy and product development.

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.

Social crimes at the village level require participatory and sustainable prevention approaches. This study aims to analyze the synergy between the Sidoarjo Police Resort through its BINMAS unit and the residents of Waruberong Village in implementing the “4D” anticipation framework (Corruption, Narcotics, Bullying, and Sexual Harassment). A descriptive qualitative method was employed, utilizing data collection through observation, semi-structured interviews, and documentation analysis of socialization activities and police-community forums. The results indicate that this partnership successfully shifts the security paradigm from a reactive enforcement model toward proactive collaboration grounded in education and early reporting. BINMAS functions effectively as an administrative liaison, coordinating Senkom Mitra Polri, village officials, and residents, thereby standardizing and streamlining report handling processes. The implementation of the 4D framework significantly enhances public legal literacy, strengthens trust in the DUMAS Presisi system, and encourages active community participation in security patrols and awareness campaigns. Although minor administrative challenges, such as limited reporting literacy and fluctuating attendance, were identified, they can be mitigated through structured mentoring. Overall, the 4D-based partnership model proves highly relevant for strengthening participatory security ecosystems at the village level. This study recommends standardizing educational modules, providing continuous administrative training for Senkom members, and integrating digital reporting platforms to ensure the program's long-term sustainability.

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.

Computers in one of the network companies at PT. Estrada uses the Fortinet operating system. The final result expected through this implementation is to comprehensively see the capabilities of the firewall on Fortinet in overcoming the problem of blocking social media applications and streaming platforms during working hours. Blocking the application in question is the ability to filter web processes such as Facebook, Instagram, YouTube, etc. In the tests carried out, web filtering was able to block applications on social media and streaming platforms, which proves that the performance of web filtering is quite good. In analyzing web filtering performance, use the office hour rule tool by carrying out the rule schedule in the Fortinet network and displaying all the information in detail. The final result obtained in the network application filtering simulation process using Fortinet is that every network sent cannot be entered (blocked) on both social media applications and streaming platforms.

The development of social media has driven changes in digital marketing communication strategies, including within educational institutions such as Juara Academy. This study aims to analyze digital marketing communication strategies through Instagram in increasing student enrollment, identify gaps between social media performance and conversion rates, and formulate efforts to optimize these strategies. This research employs a descriptive qualitative approach using a case study method. Data were collected through interviews, observations, and documentation, and then analyzed using the interactive model of Miles and Huberman, as well as the AIDA framework (attention, interest, desire, action). The results indicate that digital marketing communication strategies have been systematically implemented through the utilization of Instagram features such as feed, story, reels, and live to capture attention, build interest, generate desire, and ultimately encourage enrollment actions. However, a gap was found between high engagement levels and suboptimal enrollment conversions, influenced by technical factors such as inconsistent content posting and a lack of integration across communication stages. Strategic optimization is therefore necessary through strengthening content consistency, enhancing more personalized interactions, and leveraging persuasive approaches based on audience experience. This study provides practical contributions to the management of digital marketing communication as well as theoretical contributions to the development of social media–based marketing communication studies.  

This study aims to examine the role of customer trust as a mediating variable in the relationship between digital marketing strategies and consumer behavior. As digital marketing evolves, companies are required not only to create engaging content but also to build strong trust to influence purchase intention, loyalty, and customer engagement. This research employs a systematic literature review (SLR) following PRISMA guidelines, selecting 52 empirical articles from reputable international databases such as Scopus, Web of Science, EBSCOhost, and ScienceDirect, published between 2015 and 2026. The analysis involved thematic and descriptive synthesis to assess the strength of digital marketing’s impact on customer trust and consumer behavior outcomes. The results indicate that digital marketing has a significant positive effect on customer trust formation (β=0.652; p<0.001). Customer trust functions as a partial mediator that strengthens the impact of digital marketing on purchase intention, loyalty, and engagement. The direct effect of digital marketing on consumer behavior is weaker when trust is not optimally established. These findings emphasize that effective digital marketing strategies must focus on transparency, security, credibility, and personalization to maximize consumer behavior outcomes. Practically, this study provides important implications for companies and digital marketing practitioners, highlighting that successful campaigns rely not only on content and visibility but also on the ability to build customer trust. The study also opens avenues for future research to examine more specific trust dimensions, including platform integrity, information quality, and social proof, within increasingly complex digital marketing contexts.

This study aims to analyze the role of online sales in maintaining the financial stability of Micro, Small, and Medium Enterprises (MSMEs) in West Sumatra, Indonesia. The increasing adoption of digital channels, such as marketplaces, social media, messaging applications, live selling, and digital payment systems, has transformed online sales into not only marketing tools but also mechanisms that may affect cash flow continuity and business sustainability. Despite their growing importance, empirical evidence regarding the contribution of online sales to MSME financial stability remains limited. This study employs a quantitative explanatory research design. The population consists of MSME owners in West Sumatra who utilize online sales, while purposive sampling was used to select respondents who had engaged in online selling for at least one year and maintained cash flow records. Data were collected from 102 respondents through a structured questionnaire using a five-point Likert scale and analyzed using Partial Least Squares-Structural Equation Modeling (PLS-SEM). The findings reveal that online sales have a positive and significant effect on financial stability, with a path coefficient of 0.632, a t-value of 9.214, and a p-value below 0.001. These results indicate that effective use of online sales enhances cash flow continuity, income regularity, working capital adequacy, and financial resilience. However, the benefits depend on disciplined management of digital costs, discounts, platform fees, shipping expenses, product returns, and cash flow records. This study contributes to MSME digitalization literature by highlighting online sales as a strategic instrument for strengthening financial stability rather than merely a marketing channel.

This study aims to analyze the implementation of public service ethics by front office officers at the Public Service Mall (Mall Pelayanan Publik/MPP) of Samarinda City and its impact on the image of government institutions. As the frontline of public service delivery, front office personnel play a strategic role in shaping public perceptions and fostering public trust in government services. This research employed a qualitative descriptive approach, utilizing in-depth interviews with a functional licensing officer at the Department of Investment and One-Stop Integrated Services (DPMPTSP) of Samarinda City, supported by direct field observations. The findings reveal that the implementation of service ethics at the Samarinda MPP is guided by the principles outlined in Law No. 25 of 2009 on Public Services and the Minister of Administrative and Bureaucratic Reform Regulation No. 15 of 2014 concerning Service Standards. These regulations emphasize the values of integrity, professionalism, accountability, responsiveness, and inclusiveness in service delivery. Furthermore, effective coordination among service tenants, continuous improvement of employee competencies, and adaptation to digital systems such as the Online Single Submission (OSS) platform have contributed significantly to maintaining service quality. The study concludes that the consistent application of public service ethics enhances citizen satisfaction, strengthens public trust, and positively influences the image of government institutions in the community.  

This study aims to examine the influence of price and electronic word of mouth (e-WOM) on purchase decisions among Generation Z consumers in Campaka Subdistrict, Bandung City, with a specific focus on the TikTok Shop platform. This research employs a quantitative approach using a survey method by distributing questionnaires to Generation Z consumers in Campaka Subdistrict, Bandung City, with a total of 98 respondents to collect the data. The results of the study indicate that both price and electronic word of mouth have a significant positive effect on purchase decisions among Generation Z consumers in Campaka Subdistrict, Bandung City. Price is generally perceived as good, although aspects related to price suitability based on income levels and purchasing power still show some weaknesses. Similarly, e-WOM activity is considered positive; however, there are still relatively weak aspects regarding the diversity of consumer reviews, as not all respondents actively pay attention to the variation of opinions in reviews. The findings also reveal a significant influence of these variables, where the effect of price on purchase decisions reaches 35.4%, while electronic word of mouth contributes 29.5%, and the combined effect of price and electronic word of mouth on purchase decisions is 64.9%. This study concludes that improvements in price perception and e-WOM can enhance purchase decisions.

Technological developments have led to a shift from traditional sales promotion to digital sales promotion. A new phenomenon has emerged, namely the integration of social media platforms and e-commerce. The purpose of this study is to analyze the effect of sales promotion and positive emotions on impulsive buying, as well as the mediating role of positive emotions on TikTok Shop users in Denpasar. The results of this study were obtained using the SEM-PLS method. The results of this study indicate that positive emotions and sales promotions have a significant influence on impulsive purchasing of products on TikTok Shop, sales promotions have a significant influence on positive emotions when purchasing products on TikTok Shop, and positive emotions can mediate the influence of sales promotions on impulsive purchasing of products on TikTok Shop.  

Usability evaluation plays a crucial role in ensuring the quality of digital systems, particularly in terms of comfort, effectiveness, and ease of use. Instruments such as the System Usability Scale (SUS), User Experience Questionnaire (UEQ), and Heuristic Evaluation (HE) are widely used in modern usability studies. This research conducts a Systematic Literature Review (SLR) to identify patterns and trends in the use of these instruments. A total of 27 initial studies were collected, and 16 were selected through the PRISMA screening procedure. The findings show that UEQ is the most frequently used instrument, especially in Learning Management Systems (LMS) and academic platforms, while SUS is commonly applied to mobile applications and digital libraries for rapid usability assessment. HE is effective in revealing fundamental interface issues such as non-intuitive navigation and layout inconsistencies. Overall, digital systems perform well in Efficiency and Perspicuity, but consistently show low scores in Novelty. This study provides an integrative knowledge map that highlights cross-instrument insights and supports the development of more intuitive, innovative, and user-centered digital systems