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This study examines the effects of Net Profit Margin, audit opinion, and firm size on audit delay among construction companies listed on the Indonesia Stock Exchange from 2019 to 2025. Audit delay is measured as the period between the fiscal year-end and the issuance date of the independent auditor’s report. Timely financial reporting is particularly important in the construction sector due to its complex long-term projects, progress-based revenue recognition, cost estimation, and high financial risks. Using a quantitative approach, the study analyzes secondary data from annual financial statements and independent auditor reports. The sample includes 14 construction companies observed over seven years, producing 98 observations. Panel data regression was conducted using EViews, with the Chow, Hausman, and Lagrange Multiplier tests identifying the Random Effect Model as the most appropriate estimation method. The findings show that Net Profit Margin does not significantly affect audit delay. In contrast, audit opinion and firm size have negative and significant effects, indicating that favorable audit opinions and larger company size are associated with shorter audit completion periods. Collectively, the three variables significantly influence audit delay, although they explain only 15.75% of its variation.

Vocational high school graduates are expected to master not only technical competencies but also interpersonal skills that support their readiness for the workplace. Field observations at SMK Pasundan 1 Bandung indicate that students in the Online Business and Retail (Bisnis Daring dan Ritel) program still show a low level of assertive communication, evidenced by their difficulty in expressing opinions confidently, refusing requests politely, and handling direct interaction with customers. This community service program (Program Kemitraan Masyarakat/PKM) was designed to develop students' assertive communication skills through the role-play method. The program consists of five stages: needs analysis, development of training materials and role-play scenarios, training and practice, evaluation and feedback, and continued mentoring. Students are placed in simulated roles such as seller, buyer, and observer in scenarios involving customer service, complaint handling, price negotiation, and teamwork. The information-dissemination approach combines material delivery, discussion, hands-on practice, and technical guidance. The program is expected to increase students' understanding of assertive communication concepts, strengthen their interpersonal communication skills, and provide an applied learning experience relevant to the demands of the retail and customer-service industry. The expected output of this activity is a scientific article published in a nationally accredited (Sinta) journal.

This study aims to analyze the language style employed by President Prabowo Subianto in his speech delivered at the 17th National Farmers and Fishermen Week in 2026 using Teun A. Van Dijk's Critical Discourse Analysis framework. The study employed a qualitative approach with documentation techniques applied to the official speech transcript. Data were analyzed through Van Dijk's three analytical dimensions, namely text, social cognition, and social context. The findings reveal that, at the textual level, the speech is dominated by themes of support for farmers and fishermen as strategic actors in national development and food security. At the superstructure level, the speech is organized argumentatively through personal experiences, policy explanations, and reaffirmation of governmental commitments. At the microstructure level, the speech demonstrates the use of informal language, rhetorical strategies, repetition, and lexical choices that strengthen the relationship between the speaker and the audience. In terms of social cognition, the findings indicate the speaker's awareness of the media's role in shaping public opinion, as reflected in several instances of meta-communication. Meanwhile, the social context dimension reveals representations of power relations through the construction of social groups positioned either as supporters or obstacles to national interests. These findings suggest that the speech functions not only as a means of political communication but also as a medium for constructing ideology, legitimizing power, and shaping political identity. This study reinforces the view that language plays a significant role in representing social realities and power relations within society.

The incorporation of artificial intelligence (AI) in language education has provided new opportunities for students to practice English learning especially in speaking ability. This research explored what the students thought about speaking in English and whether or not they had positive opinions toward using AI-based learning to enhance their English speaking skill at the tenth grade of SMAN 19 Tebo. This research adopted a qualitative research design. Questionnaires and follow- up interviews with students who had used Replika AI as a tool for speaking practice were the methods of data collection. The findings indicated that students had different attitudes to speaking in English. Several students reported experiencing anxiety, lack of confidence, and fear of making mistakes when speaking in class. On the other hand, there were also some concerns regarding the tool’s artificial intelligence design of Replika AI. The interaction with an AI-service app for all participants interacting with the AI application was associated with English practice on their own terms and without feeling judged. Moreover, the program generated an interactive context where the students could practice their conversations, and build their ideas more confidently. As has been elaborated above, the results showed that Replika AI was seen as a helpful instrument to practice English speaking and reduced students’ anxiety of speaking. The study recommends that AI- mediated learning has the potential to be an alternative medium for speaking practice in the EFL context.

This study aims to synthesize empirical and theoretical literature on the Planning–Organizing–Actuating–Controlling (POAC) framework in public sector management, identify asymmetries among its functions, and propose a reconfiguration of POAC that is relevant to digital and collaborative governance. The study employs an integrative literature review by examining classical management literature, peer-reviewed journals, government regulations, and official governance indicators. Data were analyzed thematically based on the four POAC functions and synthesized across themes, using Indonesia during the 2021–2025 period as the empirical context. The findings reveal that planning and organizing functions have developed relatively well, while actuating and especially controlling remain persistent weaknesses. This condition is reflected in improvements in several formal governance indicators, including the Electronic-Based Government System (SPBE) Index, Indonesia’s ranking in the E-Government Development Index (EGDI), Unqualified Audit Opinions (WTP), and public service compliance ratings. However, during the same period, the Corruption Perceptions Index (CPI) declined. These findings indicate a gap between administrative achievements and substantive outcomes, consistent with the concept of means–ends decoupling in neo-institutional theory. The study contributes by bridging classical management theory with contemporary governance paradigms and proposing a Data-driven, Networked, Adaptive, and Participatory (DNAP) model of POAC. Practically, the results highlight the need to strengthen controlling functions and adaptive leadership to foster more effective, transparent, and outcome-oriented public governance.

The development of the internet and social media has encouraged e-commerce companies to utilize digital platforms as a marketing tool, one of which is Shopee through TikTok social media. The high frequency of digital advertising is thought to influence consumer attitudes, especially among Generation Z as active TikTok users. To determine the impact of Shopee's digital advertising frequency on consumer attitudes using promotions as a mediating variable on Generation Z TikTok social media users in Tasikmalaya, this study employed quantitative, descriptive, and verification techniques. A total of 100 respondents were sampled using purposive sampling techniques. An online questionnaire with a Likert scale of 1 to 5 was used to collect research data. Data were analyzed using Structural Equation Modeling-Partial Least Square (SEM-PLS) with the help of SmartPLS 4. The results showed that advertising frequency significantly and positively influenced consumer attitudes and promotions. In addition, promotions can mediate the effect of advertising frequency on consumer attitudes and have a beneficial and significant impact. These results indicate that consumer opinions towards Shopee will be more positive if advertisements are displayed more frequently and supported by appropriate promotions. This study serves as an academic reference and information source for businesses seeking to optimize social media-based digital marketing strategies.

Digital transformation in the military procurement sector has brought significant changes to accountability patterns at the Naval Academy (AAL). Using the AP2EP management cycle (Analysis, Planning, Execution, Evaluation, and Control) as an analytical tool, this paper dissects the extent to which the E-Procurement system can mitigate budget deviation risks and enhance financial transparency. As a military educational institution striving to become a World Class Naval Academy, AAL faces unique challenges in balancing state financial regulations with specific educational logistics needs. Through a descriptive qualitative approach, this research demonstrates that procurement digitalization provides an automated audit trail that minimizes human intervention. Despite technical and cultural obstacles, strategic steps such as developing real-time dashboards have proven effective in optimizing state financial governance to support cadet education quality and maintain an Unqualified Opinion (WTP).

This study aims to describe the implementation of the group discussion model as a strategy to strengthen students' collaboration in Indonesian procedural text learning in Grade IV of SDN 8 Selat Penuguan. The study focuses on the implementation of group discussion, the forms of students' collaboration, the teacher's role in guiding collaboration, and the supporting and inhibiting factors in the learning process. This research employed a descriptive qualitative approach. The primary data were obtained from the teacher and Grade IV students, while supporting data were collected from learning documents. Data were gathered through observation, interviews, and documentation, and were analyzed through data reduction, data display, and conclusion drawing. Data validity was strengthened through source and technique triangulation. The findings indicate that group discussion was implemented through planning, small-group formation, procedural-text-based tasks, discussion, presentation, feedback, and reflection. The model strengthened students' collaboration through task sharing, mutual assistance, expressing opinions, listening to peers, and taking responsibility for group outcomes. The teacher acted as a learning designer, facilitator, guide, communication mediator, and evaluator of collaborative processes. Supporting factors included teacher readiness, the suitability of procedural text material, the use of worksheets, and a conducive classroom climate, while inhibiting factors included uneven student participation, limited time, differences in language competence, and the dominance of particular students in group work. The study concludes that the group discussion model is relevant for procedural text learning because it integrates material comprehension with the development of elementary students' social skills.

Document-level sentiment analysis assigns a single polarity label to an entire review, often obscuring opinion diversity within multi-sentence submissions. This limitation is particularly evident in reviews of multi-service platforms, where users frequently express heterogeneous opinions toward different aspects of the platform in the same review. To address this challenge, this study proposes a sentence-level sentiment analysis framework for Indonesian Gojek app reviews collected from the Google Play Store. The proposed framework introduces a two-stage segmentation strategy that combines punctuation-aware rules with conjunction-aware splitting based on coordinating and adversative conjunctions (e.g., tapi [but], padahal [even though]) to identify opinion boundaries and decompose mixed-sentiment reviews into independently classifiable sentence units. A total of 14,730 raw reviews collected between May and July 2025 were subjected to data cleaning and quality filtering, resulting in 7,187 valid reviews that were further segmented into 14,187 sentence-level instances. Each instance was manually annotated by three annotators using a four-class labeling scheme consisting of app-positive, app-negative, app-neutral, and service categories. Sentiment-level inter-annotator agreement, computed on the subset of instances unanimously categorized as app-related by all three annotators (n = 4,384), achieved substantial agreement (Fleiss'  = 0.636). Hyperparameter optimization was conducted using Optuna with the Tree-structured Parzen Estimator (TPE) sampler across four experimental scenarios. The best performance was achieved by IndoBERTweet under Stratified K-Fold evaluation, attaining an accuracy of 0.751 and a macro F1-score of 0.729, outperforming all IndoBERT configurations. The results demonstrate the effectiveness of domain-adaptive pre-training on informal Indonesian text and highlight the value of conjunction-aware segmentation for preserving fine-grained opinion structures in mixed-sentiment reviews. These findings suggest that domain-aligned language representations provide a practical and effective solution for sentence-level sentiment analysis of Indonesian app reviews.

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.

Communication within the family, especially between parents and children, is an important foundation for the development of a child's personality, emotions, and social skills. The purpose of this writing is to identify the types of parental communication styles, analyze the factors that influence them, examine their impact on child development, and provide practical recommendations to improve the effectiveness of communication patterns within the family. The method used in writing this paper is a literature study. The discussion results show that there are three main types of parental communication styles: permissive, authoritarian, and democratic. The permissive style tends to give the child unlimited freedom, the authoritarian style emphasizes control and obedience, while the democratic style encourages openness and mutual respect. The choice of communication style is influenced by various factors such as educational background, culture, past experiences, and the socio-economic conditions of parents. Each communication style has a different impact on the emotional, social, and cognitive development of children. The democratic style is considered the most effective in optimally supporting child development. This article provides recommendations to parents to increase knowledge about dialogical-based parenting, practice assertive communication skills, give children space to express their opinions, and build a family culture that is open and mutually respectful.

Communication is an important aspect in the development of children aged 5–6 years, especially in supporting their language and social-emotional abilities. This study aims to analyze the influence of effective communication through a play approach in early childhood based on the results of previous research. The method used in this study is a quantitative method by conducting observations, research, and observations on effective communication and a play approach in early childhood education. Data collection techniques were carried out through documentation and literature searches from journals, articles, and relevant scientific references. Data analysis techniques used quantitative descriptive analysis by comparing and summarizing the results of previous research. The results show that the play approach has a significant influence on improving children's effective communication skills, such as the courage to speak, the ability to express opinions, social interaction, cooperation, and self-confidence. In addition, play activities have been proven to be able to create a fun learning atmosphere so that children are more active in interacting with peers and teachers. Thus, the play approach can be an effective strategy in developing the communication skills of children aged 5–6 years.

The development of digital media has transformed virtual public spaces into major arenas for shaping public opinion on religious issues, including Islamic law and sharia in Indonesia. Discussions surrounding sharia on social media are frequently accompanied by stigma and misperceptions influenced by media framing, digital algorithms, and identity polarization. This study aims to analyze the construction of stigma toward Islamic law in the digital era, identify forms of sharia misperception in the Indonesian public sphere, and explain factors contributing to the reproduction of such stigma. This research employs a qualitative approach based on an integrative literature review combined with digital media discourse analysis. Data were collected through scientific literature reviews, social media observations, and analysis of digital content related to sharia discourse. The findings reveal that sharia is often associated with violence, anti-democracy, restrictions on women’s rights, and opposition to modernity due to media simplification and emotionally driven digital content. In addition, low levels of religious digital literacy and the prevalence of echo chambers reinforce the spread of stigma toward Islamic law in virtual public spaces. This study emphasizes the importance of strengthening religious digital literacy and promoting moderate and inclusive Islamic narratives in contemporary digital society.

This study examines sarcastic implicatures in the 2024 United States presidential debate between Joe Biden and Donald Trump, with a particular focus on Donald Trump’s utterances. The study aims to identify the forms and types of sarcastic implicatures employed in political discourse during the debate. A qualitative descriptive method with a pragmatic approach was used to analyze how implied meanings are constructed and interpreted within the context of political communication. The data consisted of debate transcripts and video recordings broadcast by CNN, selected based on utterances containing elements of sarcasm. Data analysis was conducted through four stages: identification, classification, coding, and interpretation. The findings reveal that sarcastic implicatures are realized in two main forms, namely indirect non-literal utterances and direct non-literal utterances. In addition, several types of sarcastic implicatures were identified, including undermining, mockery, insult, criticism, and threat. The most dominant type was undermining, which was used to weaken the image and credibility of political opponents. These findings indicate that sarcastic implicatures function as an effective rhetorical strategy in political communication to influence public opinion, shape audience perceptions, and strengthen the speaker’s political position in televised political debates.

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.

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 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.

Rapid technological developments and social changes in the disruption era require organizations, including educational institutions, to adapt quickly and innovatively. This article discusses the importance of building psychological safety as a foundation to enhance team innovation and adaptability. Psychological safety is a condition in which team members feel safe to express ideas, ask questions, and share opinions without fear of negative consequences. The study employs a qualitative literature review method; analyzing various scientific sources related to psychological safety, leadership, and organizational management. Findings indicate that supportive and inclusive leadership plays a crucial role in fostering a psychologically safe environment, which in turn promotes open communication; effective collaboration, and innovative behavior. Developing psychological safety enhances intrinsic motivation, job engagement, and learning agility; enabling teams to respond effectively to rapid organizational changes. This research implies that organizations should prioritize psychological safety to strengthen team performance and sustainability in the dynamic disruption era.

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.