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This study investigates how Lembaga Musik Pervagatus, a group that does not display Islamic identity, creates an Islamic musical image when performing at religious events in Islamic campuses.  This study investigates how visual elements, song selection, and stage interaction influence audience perception. It does so using qualitative methods through interviews and performance observations.  How the band's performative identity is influenced by attributes such as appearance, personality, cultural values, and audience relations is studied using Kapferer's Brand Identity Prism model.  Conversely, Stuart Hall's theory of representation helps explain how Islamic meaning emerges in the context of events. The results show that the image of Islamic music attached to Pervagatus does not originate from the band's original identity; rather, it is a construction of meaning influenced by the context of religious events and the audience's interpretation of the song ‘Maulana Ya Maulana’ that they performed.  This perception was further reinforced by their neat, polite, and enthusiastic appearance.  The results show that the relationship between performance, context, and audience interpretation shapes the image of Islamic music on campus.

This study examines how children and their relationship with nature are represented through the character of Antonio in the film Encanto (2021). Antonio is portrayed as a child who can communicate with animals, reflecting innocence, empathy, and a spiritual closeness to nature. This study aims to describe how children's relationship with nature is depicted in the film and to interpret the symbolic meaning behind these interactions. The method used in this study is a descriptive, qualitative approach that draws on theories of child development, symbolism, and children's imagination. The results of this study show that among children, Antonio reflects a harmonious, loving, and open attitude towards other life forms. Animals in the film serve as moral and emotional symbols that reflect the nature of children: the jaguar symbolizes courage, the toucan symbolizes honesty and cheerfulness. In contrast, the capybara symbolizes peace of mind. This study shows that children's films can be a tool for building ecological awareness and social empathy through imagination. The implications of this study underscore the importance of strengthening empathy and environmental balance in children's education through literature and film.

The animated film KPop Demon Hunters quickly became a worldwide hit, reaching the top of Netflix's global charts with over 33 million views in just two weeks. The highly acclaimed film KPop Demon Hunters has been the subject of some research due to its notable accomplishment. Yet, none of the research above discusses the symbolism that is richly put in the movie. Thus, the research attempts to elaborate on the representation of symbolism in order to provide an alternative viewpoint of the Kpop Demon Hunters, particularly the well-known villain, the Saja Boys. In order to study the underlying meaning of the Saja Boys, the author employs the qualitative method and applies Vrabel's study of deadly sin in relation to the religious symbol. This study concludes that the members of the Saja Boys represent symbolism of the deadly sins, which are anger, envy, greed, lust, pride, gluttony, and sloth. These vices are shown from the behavior of the Saja Boys, such as barking towards fans aggressively, being envious of one’s freedom and happiness, stocking food as much as possible, giving sexual gratification through physical appearance, and being overly proud of it. Not only that, the members of Saja Boys also portrayed the action of overconsumption and laziness.

The evaluation of Indonesian language instruction at the junior secondary level remains predominantly oriented toward lower-order thinking skills, thereby constraining the advancement of students’Higher Order Thinking Skills (HOTS). This situation necessitates the development of comprehensive assessment tools that are coherently aligned with the Merdeka Curriculum. The present study introduces a model for constructing HOTS-oriented assessment instruments targeting procedural text writing in Phase D. The development process was systematically organized through several stages: translating learning outcomes into measurable indicators, preparing assessment blueprints, creating item cards, formulating scoring rubrics for attitudes, knowledge, and skills, and finally analyzing and interpreting the assessment data. These instruments were specifically designed to capture students’ analytical, evaluative, and creative abilities in producing procedural texts that are logical, coherent, and effective. Findings demonstrate that the instruments provide an objective and holistic representation of students’ competency attainment. In summary, the proposed HOTS-based assessment model for procedural text writing is practical, methodical, and contextually relevant, serving as a valuable framework for evaluating Indonesian language learning in accordance with the Merdeka Curriculum principles.

The increasing volume of online hotel reviews provides valuable insights into customer perceptions but poses challenges for manual analysis due to its unstructured nature. This study aims to compare the performance of Recurrent Neural Network (RNN) and Bidirectional Encoder Representations from Transformers (BERT) in hotel review sentiment analysis. A total of 20,491 TripAdvisor hotel reviews were classified into three sentiment categories: negative, neutral, and positive. The research methodology includes text preprocessing, stratified data splitting, class imbalance handling using Random Over-Sampling, tokenization, and supervised model training. Model performance was evaluated using a confusion matrix and classification metrics. The results indicate that BERT outperforms RNN, achieving an accuracy of 80.54%, while RNN reached 62.21%. BERT demonstrated superior capability in capturing contextual and semantic information in hotel reviews. These findings suggest that transformer-based models are more effective for sentiment analysis of complex textual data in the hospitality domain and can support data-driven service improvement strategies.    

The rapid growth of e-commerce platforms in Indonesia, particularly Tokopedia, has resulted in a large volume of consumer reviews containing valuable information regarding customer perceptions and satisfaction. However, manual analysis of such reviews is inefficient and prone to subjectivity, necessitating an automated approach based on machine learning. This study aims to classify the sentiment of sports product reviews on Tokopedia into positive, negative, and neutral categories by applying Logistic Regression, Support Vector Machine (SVM), and Random Forest using the Term Frequency–Inverse Document Frequency (TF-IDF) approach. The data were collected through web scraping of Indonesian-language sports product reviews and processed through several preprocessing stages, including data cleaning, case folding, tokenization, stopword removal, and stemming. Feature representation was performed using TF-IDF to transform textual data into numerical vectors, after which the dataset was divided into training and testing sets with an 80:20 ratio. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics. The results indicate that the application of TF-IDF significantly improves the performance of all models, with SVM consistently achieving the most optimal performance compared to Logistic Regression and Random Forest. These findings demonstrate that classical machine learning algorithms combined with TF-IDF remain highly effective for sentiment analysis of Indonesian-language text. The implications of this study are expected to assist sellers in understanding customer opinions, support consumers in making informed purchasing decisions, and serve as a foundation for the development of sentiment analysis and recommendation systems on e-commerce platforms.

With the rapid growth of Jambi City's tourism sector, the demand for souvenir products as a representation of local culture has increased. However, this potential has resulted in fierce competition among businesses. To generate new customers, businesses require effective digital and physical marketing strategies. The purpose of this study was to examine how social media marketing, product quality, and location at Gerai Adila simultaneously and partially influence consumer purchasing decisions. A survey method was used to implement a quantitative approach; data from 387 respondents, collected through Google Forms, were selected using the Slovin formula from a population of 11,723 customers in 2024. In data analysis, instruments, classical assumptions, multiple linear regression, and hypothesis testing were conducted using SPSS 25. The results showed that social media marketing, product quality, and Gerai Adila's location partially had a positive and significant impact on consumer decisions to purchase products at Gerai Adila; social media marketing played a role in increasing consumer brand awareness and purchasing interest, while product quality influenced consumer satisfaction and trust. In addition, it was proven that all three variables had a positive and significant impact on purchasing decisions. The results show that to increase the competitiveness of souvenir shops in Jambi City, digital marketing strategies, consistent product quality, and the right location are important factors.

The growth of e-commerce in Indonesia has led to an increase in product reviews, including for beauty products on Tokopedia and Shopee. These reviews serve as important sources of information to assess consumer satisfaction; however, manually analyzing thousands of reviews daily is impractical. This study applies Natural Language Processing (NLP) with Naive Bayes, C4.5, XGBoost algorithms to classify sentiment in Indonesian-language reviews. The dataset used consists of 76,256 reviews labeled as positive, negative, and neutral. The research stages include text preprocessing, feature representation using BoW and TF-IDF, data balancing through SMOTE, and model performance evaluation based on accuracy, precision, and recall. Differences in results among the algorithms were analyzed using ANOVA. The results show that Naive Bayes achieved the highest accuracy at 67.71%, followed by XGBoost at 65.91%, and C4.5 at 58.39%, with Naive Bayes performing best in identifying positive and negative sentiments, while XGBoost and C4.5 handled more complex data patterns effectively. These findings provide guidance for sentiment analysis in Indonesian and support businesses in obtaining automated insights from customer reviews to improve product quality and services.

The increasing social and cultural diversity of contemporary societies has positioned schools as strategic spaces for nurturing inclusive attitudes and managing the challenges of pluralism. Many schools still face obstacles such as limited teacher readiness, inadequate curriculum representation of diversity, and school cultures that are not fully supportive of inclusive practices. This study aims to analyze school strategies in fostering students’ inclusive dispositions in response to pluralistic challenges. The research employed a descriptive qualitative approach through library research, using purposive sampling to select relevant scientific literature. Data were analyzed using an interactive model that involved data reduction, data display, and conclusion drawing. The findings indicate that participatory learning strategies, the application of Universal Design for Learning, and project-based learning are effective in strengthening students’ inclusive attitudes, empathy, and tolerance. The role of teachers as role models, the development of inclusive school culture, and active community involvement were identified as key supporting factors for successful inclusive education. The implications of this study highlight the importance of synergy between teacher professional development, curriculum adaptation, and school policy reinforcement to establish equitable, inclusive, and sustainable educational practices in pluralistic contexts.

This study aims to describe the forms and social functions of code-switching and code-mixing in the film Hujan Bulan Juni and to explain how language choice represents identity and social differences among the characters. The film was chosen because it portrays interactions between characters from different cultural backgrounds Javanese and Manadonese which allows for complex patterns of language contact. The study employed a descriptive qualitative method using observation and note-taking techniques on dialogues containing Indonesian, Javanese, and Manadonese linguistic elements. The data were analyzed using the sociolinguistic frameworks of Gumperz (1982) and Wardhaugh (2015) to uncover the social functions and identity meanings reflected in the speakers’ language choices.The findings reveal that code-switching from Indonesian to Manadonese serves to reinforce ethnic solidarity, emotional closeness, and cultural pride, while switching from Indonesian to Javanese reflects politeness, intimacy, and social hierarchy. Meanwhile, code-mixing between Indonesian and Manadonese marks distinctive speech styles and expresses regional identity, whereas code-mixing between Indonesian and Javanese functions to soften utterances and portray the speakers’ courteous demeanor. Overall, language choice in Hujan Bulan Juni is not merely a communicative tool but also a representation of identity and social differentiation within Indonesia’s multilingual society.

This study investigates the relationship between Environmental, Social, and Governance (ESG) performance and corporate financial distress, with board gender diversity examined as a moderating variable. Using 96 firm-year observations from manufacturing companies listed on the Indonesia Stock Exchange (2022–2024), the analysis employs variance-based Structural Equation Modelling (SEM). The findings reveal that ESG performance does not exert a statistically significant effect on financial distress, and gender diversity does not moderate this relationship. These non-significant results constitute the central empirical contribution of the study, highlighting that ESG engagement and gender diversity have yet to translate into financial resilience in the Indonesian manufacturing context. The study underscores the importance of contextual factors—such as implementation costs, authenticity of ESG disclosures, and limited female representation on boards—in shaping the effectiveness of sustainability practices. The results provide theoretical implications for Stakeholder and Agency Theory and offer practical insights for managers, regulators, and investors in emerging markets.

This study discusses the role of the Kupang City Council in carrying out its supervisory function on gender-responsive maternal and child health policies, with a case study focus on Oebobo District. The background of this study stems from the high maternal and child mortality rates and the suboptimal implementation of Kupang City Regulation No. 7 of 2013. This study uses a qualitative approach with interview, observation, and documentation techniques, and involves informants from DPRD members, health workers, posyandu cadres, and community leaders. The results show that the DPRD, particularly through Commission IV, has carried out its supervisory function through regulatory support, budget allocation, program evaluation, and field visits. Some of the programs supervised include Free Nutritious Meals (MBG), strengthening Posyandu, and basic health services at Puskesmas. However, the effectiveness of supervision still faces various obstacles, including the lack of female representation in the DPRD, strong patriarchal culture, lack of socialization of local regulations, weak cross-sector coordination, and low community participation. The conclusion of this study confirms that the success of gender-responsive maternal and child health policies is greatly influenced by the capacity of the DPRD in oversight, political and budgetary support, and community involvement.

This study investigates the impact of board characteristics on the financial performance of non-financial companies listed in the JII70 index in Indonesia. Motivated by the ongoing debate on the effectiveness of corporate governance mechanisms in enhancing firm outcomes, particularly within Sharia-compliant markets, this study focuses on three key board attributes: board size, board independence, and female representation on the board. Using a quantitative causal approach and panel data from 25 companies over the period 2020–2023, the study employs a fixed effect model to evaluate the relationship between board structure and financial performance measured by Return on Assets (ROA). The results show that board size has a positive and significant effect on firm performance, indicating that larger boards may enhance oversight capacity and provide broader resources beneficial to strategic decision-making. Conversely, board independence and board female representation do not exhibit significant effects on financial performance, suggesting that their roles may be more symbolic or constrained by institutional and contextual factors in the sampled companies. These findings highlight the importance of understanding corporate governance not merely in structural terms, but in relation to functional effectiveness and contextual maturity. The study offers implications for regulators, companies, and governance reform initiatives, particularly regarding strengthening substantive roles of independent and female commissioners in improving firm performance within Sharia-compliant markets.

Abstract: The rapid diffusion of Generative Artificial Intelligence (AI) has intensified public debate regarding its benefits, risks, and societal implications. This study investigates public sentiment and thematic structures surrounding Generative AI by analyzing Twitter discourse as a representation of large-scale, real-time public perception. The research addresses two main problems: how public sentiment toward Generative AI is distributed and what dominant themes shape this perception. Accordingly, the objective is to map both emotional polarity and thematic narratives embedded in social media conversations. A computational mixed-methods approach was employed using a dataset of 12,470 tweets collected on 17 December 2024. Sentiment classification was conducted using a transformer-based DistilBERT model, while semantic representations were generated with Sentence-BERT. Topic modeling was performed using BERTopic, integrating HDBSCAN clustering and class-based TF-IDF to extract coherent and interpretable topics. Human-in-the-loop validation supported the interpretive robustness of topic labeling. The findings reveal that public sentiment toward Generative AI is predominantly positive (41.8%), particularly in relation to productivity enhancement, education, and creative applications. Neutral sentiment (31.4%) reflects informational discourse, while negative sentiment (26.8%) centers on ethical concerns, privacy risks, misinformation, and AI hallucinations. Seven dominant topics were identified, with clear topic–sentiment alignment showing optimism in utility-driven themes and skepticism in ethics- and risk-related discussions. In conclusion, public perception of Generative AI is dualistic—characterized by strong enthusiasm alongside persistent caution. These results provide empirical insights for AI governance, responsible innovation, and future research on socio-technical impacts of Generative AI. *    

: The rapid expansion of cybersecurity standards and threat intelligence frameworks has led to significant semantic fragmentation among security terminologies, hindering effective information retrieval and interoperability across systems. Traditional keyword-based search approaches are inadequate for capturing the contextual meaning of security terms, particularly within formal frameworks such as NIST, MITRE ATT&CK, and CWE. This study addresses this challenge by proposing CyberBERT, a transformer-based semantic search framework designed to align cybersecurity terminologies through deep contextual representation and ontology-driven reasoning. Research Objectives: The primary objective of this research is to develop a semantic retrieval model capable of understanding conceptual relationships between security terms beyond lexical similarity. Methodology: The proposed methodology fine-tunes a BERT-based model on the NIST Glossary corpus using a combination of masked language modeling and triplet loss objectives to generate discriminative semantic embeddings. These embeddings are further aligned with cybersecurity ontologies, including MITRE ATT&CK and CWE, to enhance semantic consistency and explainability. Semantic retrieval is performed using cosine similarity within a 768-dimensional embedding space and evaluated using Mean Reciprocal Rank (MRR) and Precision@K metrics. Results: Experimental results demonstrate that CyberBERT achieves an MRR of 0.832, outperforming domain-adapted baselines such as SecureBERT and CyBERT. The integration of ontology alignment improves semantic accuracy by over 6%, while robustness evaluations confirm resilience against adversarial linguistic perturbations. Visualization using t-SNE reveals coherent semantic clustering aligned with the five core NIST Cybersecurity Framework functions. Conclusions: In conclusion, CyberBERT effectively bridges semantic gaps across cybersecurity terminologies by combining transformer-based contextual learning with ontological reasoning. The framework offers a robust, interpretable, and scalable solution for semantic search, supporting improved interoperability and knowledge discovery in cybersecurity operations and standards harmonization.

The development of value-added corn-based beverages representation an emerging opportunity within the agribusiness sector, particularly for student-led entrepreneurial initiatives seeking to utilize local agricultural resources. This study examines consumer perceptions of Zeagrain corn milk, a sweet-corn-derived drink positioned as a healthy, practical, and affordable alternative within the ready-to-drink market segment. The research aims to identify key attributes influencing consumer interest, evaluate perceived product performance, and assess the potential competitiveness of Zeagrain as a new agribusiness product. Data were collected using an online quantitative survey targeting respondents aged 17–35 years who represent potential urban consumers. The questionnaire measured demographic characteristics, consumption patterns of similar beverages, perceived importance and performance of product attributes, and purchase intention. The findings are expected to provide insights into consumer expectations regarding taste, packaging, nutritional value, and price. The results further serve as empirical input for improving product development strategies and enhancing the market readiness of Zeagrain. Ultimately, this study contributes to strengthening the commercialization prospects of corn-based beverages and supports innovation efforts among young agripreneurs seeking to optimize local commodities.  

Social care values ​​are moral principles that emphasize empathy, altruism, solidarity, mutual cooperation, justice, and tolerance in social life. These values ​​form the foundation for individuals to understand the feelings of others, help selflessly, cooperate, be fair, and respect differences. This study aims to describe the representation of social care values ​​in Wattpad short stories in the family drama genre, identify the most dominant values ​​and the context in which they emerge, and explain the relevance of these findings to learning and character development in students. The study used a descriptive qualitative method because it focuses on the meaning of the text, rather than quantitative measurement. Data were collected through listening and note-taking techniques by carefully and repeatedly reading the short stories to identify sections of the text that contain social care values. Data included words, phrases, sentences, dialogue, and narratives that depict character actions, conflicts, or interactions reflecting empathy, altruism, solidarity, mutual cooperation, justice, and tolerance. All quotations were recorded on a coding sheet to organize the data systematically. Data analysis was conducted using the Miles and Huberman technique, which includes data reduction, classification, and interpretation. In the reduction stage, relevant data was selected and then grouped according to social awareness value categories. Next, interpretation was conducted to explain the representation of these values ​​in the storyline and relationships between characters. 

This study develops an architectural design framework integrating fun and playful spatial characteristics with futuristic, science-based approaches to enhance children’s engagement with science and technology learning. Traditional learning environments are often rigid and fail to stimulate curiosity, especially among children aged 7–11. Utilizing a qualitative conceptual analysis, this research synthesizes architectural theory, neuroscience learning principles, children’s spatial psychology, and science-based design methodologies. The results show that playful spatial elements—dynamic forms, color contrasts, sensory interactions, and imaginative environments—significantly improve motivation, exploration, and conceptual understanding. Futuristic design components further support scientific literacy by projecting technological possibilities and integrating interactive digital tools. The study produces a conceptual architectural model linking playfulness, scientific experience, and future-oriented visualization. It concludes that learning environments must be intentionally designed to merge emotional engagement, cognitive stimulation, and technological representation. The implications extend to the design of children’s museums, science centers, and early STEM learning facilities.

This study aims to investigate the empirical impact of population size and unemployment rates on economic growth in Central Kalimantan Province. The research focuses on the regional context of Central Kalimantan, utilizing a longitudinal dataset covering the eleven-year period from 2013 to 2024. To achieve the research objectives, the study employs quantitative analysis through the SPSS software package, utilizing multiple linear regression as the primary analytical tool to examine the relationship between the dependent variable (economic growth) and the independent variables (population and unemployment). The findings of the analysis reveal a divergent impact between the two independent variables. The results indicate that the population has a significant and positive influence on economic growth in Central Kalimantan during the 2013-2024 period, suggesting that demographic factors play a crucial role in regional expansion. Conversely, the unemployment rate was found to have no significant effect on economic growth within the same timeframe. Furthermore, the study conducted a comprehensive suite of classical assumption tests to ensure the validity and reliability of the statistical model. The results of these diagnostics confirm that the regression model adheres to the assumption of normality, shows no evidence of multicollinearity among the variables, and is free from symptoms of heteroscedasticity. Additionally, the analysis concludes that the model does not exhibit any issues related to autocorrelation. Consequently, the regression model is deemed statistically robust and appropriate for providing an accurate representation of the economic dynamics in Central Kalimantan.

This research aims to analyze the effect of CEO power and board gender diversity on modern slavery disclosure (MSD) among Indonesia’s top 50 publicly listed companies by market capitalization. The research uses a quantitative approach with secondary data collected from annual and sustainability reports during the 2022–2024 period. The results show that CEO power has a negative and significant effect on MSD, indicating that stronger CEO power will reduce disclosure transparency. Furthermore, gender diversity on the board of commissioners also shows a negative and significant relationship with MSD, indicating that female representation in supervisory roles has not yet contributed into greater social accountability within Indonesian firms. Meanwhile, gender diversity on the board of directors shows no significant effect. These results suggest that internal governance factors such as CEO power and limited female influence in top positions still hinder companies from being transparent about social and ethical issues. In conclusion, stronger regulations and independent oversight are needed to improve companies’ transparency regarding modern slavery practices.