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Measurement is a fundamental domain of mathematics that connects formal mathematical concepts with everyday experiences. Despite its practical relevance, students often experience persistent difficulties in understanding measurement conceptually, tending to approach it as a procedural activity rather than as a process of reasoning about quantities, units, and comparisons. These challenges indicate that learning measurement is influenced not only by individual cognitive factors but also by the social dynamics that shape classroom learning environments. This study aims to examine classroom social dynamics in learning measurement by providing empirical evidence from contextual learning situations. This study employed a qualitative research approach to explore how social interaction, scaffolding, and participation mediate students’ understanding of measurement in contextual learning environments. Data were collected through classroom observations, video recordings of learning activities, analysis of students’ written work, and interviews with selected students and the teacher. Contextual measurement tasks were designed to encourage collaboration, dialogue, and justification, enabling the examination of student–student and teacher–student interactions as they naturally occurred in the classroom. Data analysis was conducted iteratively to identify patterns of interaction, forms of scaffolding, and students’ learning progression within the Zone of Proximal Development. The findings reveal that students initially engaged with measurement tasks in a predominantly procedural manner, with limited conceptual understanding and minimal peer interaction. After the implementation of contextual learning situations, classroom social dynamics changed substantially. Students became more actively involved in discussion, collaborative problem-solving, and collective meaning-making. Peer interaction supported the articulation and refinement of students’ reasoning, while teacher scaffolding guided learning by extending students’ thinking without providing direct solutions. These social processes facilitated students’ movement from their actual level of understanding toward higher levels of conceptual competence within the Zone of Proximal Development. The study further shows that contextual learning tasks alone are insufficient to promote meaningful understanding unless they are supported by productive social interaction and adaptive scaffolding. Conceptual understanding of measurement emerged through socially mediated processes rather than through task completion alone. This study contributes to mathematics education research by emphasizing the central role of classroom social dynamics in contextual learning and by offering insights into how interaction and scaffolding can be orchestrated to support students’ conceptual understanding of measurement.

This article examines the typology and classification of regional government apparatus in Indonesia by analyzing their structure and functions within local governance. The study draws on recent theoretical literature (2020–2025) and legal frameworks to map how different types of regional apparatus such as executive agencies (“dinas”), supporting agencies (“badan daerah”), secretariat, inspectorate, and territorial units are organized and classified. Using a normative-juridical and conceptual approach, the paper reviews relevant laws, regulations, and academic studies to identify patterns of structural typology and functional differentiation within local governments. The findings reveal that many local governments still apply structural-heavy models rather than functionally tailored organizations, leading to excessive bureaucracy and inefficiency. The analysis suggests that a clearer classification aligned with functional roles can improve governance effectiveness and administrative efficiency. The article concludes by recommending that local governments re-evaluate their organizational structures to better reflect the functional needs of governance, rather than merely replicate structural models.                                                               

Rapid adoption of cashless payments has reshaped everyday spending, especially among young consumers. While e-wallets deliver speed and convenience, constant exposure to discounts, cashbacks, and one-click checkouts may also foster more frequent discretionary purchases. This study examines the relationship between e-wallet usage intensity and consumptive lifestyle, focusing on how convenience and promotional stimuli relate to spending tendencies. Using a cross-sectional online survey of students and early-career workers, we collected self-reports on payment habits and consumption patterns with validated Likert-type instruments. Data were screened and analyzed with correlation and linear regression after basic assumption checks. The results indicate a positive and statistically meaningful association between e-wallet usage and consumptive lifestyle; respondents who transact more often via e-wallets tend to report stronger preferences for instant gratification, hedonic purchases, and impulse buying. Convenience features (e.g., stored cards, fast checkout) and promotional exposure (e.g., limited-time deals) emerged as salient correlates of the relationship. The findings add contextual evidence from Indonesia’s digital economy and suggest practical implications for users, platforms, and educators. Financial-wellbeing interventions such as digital budgeting tips, in-app nudges, spend limits, or post-purchase reflections may help align seamless payments with healthier consumption decisions. Future work can test causal mechanisms and evaluate design features that encourage prudent, goal-consistent spending without diminishing user experience.

This article develops a conceptual model that explains how social capital and digital literacy interact in shaping Islamic financial literacy in the digital era. Through a comprehensive literature review, this study synthesizes theories, empirical findings, and thematic patterns derived from reputable academic journals, scholarly books, and institutional publications. The analysis shows that social capital functions as a value foundation encompassing trust, collective norms, and behavioral orientations that influence individuals’ initial acceptance of sharia-based financial practices. Information obtained through family, religious communities, and social networks becomes a crucial entry point that shapes early perceptions and preferences toward Islamic financial products. Meanwhile, digital literacy strengthens individuals’ ability to access, evaluate, and verify Islamic financial information independently through various digital content such as online articles, infographics, educational videos, and Islamic fintech platforms. The interaction between these two dimensions creates a layered learning process in which social capital provides contextual value and trust, while digital literacy deepens technical understanding in a more objective manner. This article contributes theoretically by proposing the Social Capital–Digital Literacy Integrative Model and offers practical implications for Islamic financial institutions, regulators, and fintech providers in designing more effective strategies to enhance Islamic financial literacy in society.

This study aims to systematically analyze the influence of Work Family Conflict (WFC) and Work Life Balance (WLB) on employee performance through a Systematic Literature Review (SLR) approach. The increasing demands of work and family roles in modern organizational settings make these variables highly relevant in organizational behavior research. The SLR procedure was conducted based on guidelines by Kitchenham and Wahono, consisting of planning, conducting, and reporting stages. Literature searches were performed across several academic databases, including Google Scholar, Garuda Ristek, ResearchGate, and university journal portals, covering publications from 2020 to 2025. From a total of 5,992 identified articles, 28 studies met all inclusion and quality criteria and were reviewed in depth. The findings indicate that Work Family Conflict consistently exerts a negative effect on employee performance by increasing stress, reducing concentration, and impairing psychological well-being. Conversely, Work Life Balance demonstrates a significant positive influence on performance, as employees who successfully balance their professional and personal responsibilities tend to exhibit higher productivity, job satisfaction, and engagement. Furthermore, factors such as organizational support, flexible working arrangements, and job satisfaction are shown to mediate or moderate the relationship between WFC, WLB, and employee performance. This study contributes by mapping the latest empirical patterns and providing recommendations for organizations to develop supportive work policies that enhance employee well-being and sustainable performance.

Digital entrepreneurship has become a transformative phenomenon in the modern economy, changing the way businesses are developed and run through digital technology. Although the literature on digital entrepreneurship is growing rapidly, comprehensive mapping of the evolution of knowledge and research patterns is still limited. This study analysis the development of digital entrepreneurship in academic literature through bibliometric analysis of the Scopus database to identify research trends, knowledge structures, and future directions. The analysis was conducted on 1,815 publications from 681 sources (1988-2025) written by 8,023 researchers, using R Studio (bibliometrix) and VOSviewer. The results show exponential growth with an annual growth rate of 17.22%, as well as five main theme clusters: digital business models, digital innovation, social entrepreneurship, e-commerce, and fintech. Thematic evolution shifted from traditional entrepreneurship (1988–2010) to digital transformation (2011-2018) to new technology integration (2019-2025). The level of international collaboration reached 26.39%, with the United States, China, and the United Kingdom as the main contributors. These findings provide a research roadmap for identifying research gaps, collaboration opportunities, and trending topics in digital entrepreneurship.

Climate risk finance has emerged as an increasingly important field of research along with the growing urgency to address climate change and its impacts on the global financial system. Climate change poses real risks to the stability of the international economy and financial systems. Climate risk finance represents an approach that encompasses various financial instruments in supporting climate change mitigation and adaptation. Although the term climate risk finance has not been widely used explicitly as a single keyword, the concept that integrates climate risk and financing is reflected in related keywords such as climate risk, climate finance, and climate change. This study employs a bibliometric analysis method using the Scopus database, supported by analytical tools such as VOSviewer and R Studio, to explore the development of research on climate risk finance. The study identifies publication patterns, international collaborations, and emerging themes within the related literature. The findings show that the publication rate on climate risk finance is relatively moderate each year, but has experienced growth in the last decade. The evolving understanding in this field is expected to strengthen the resilience of financial systems and support sustainable strategies to address long-term climate risks.

Green innovation has become a crucial approach to addressing sustainability challenges within global economic and environmental contexts. This study maps the development of green innovation research through a bibliometric analysis using data from the Scopus database covering the period 2021–2024. Bibliometric techniques were applied with VOSviewer and R Studio to examine publication trends, citation patterns, author collaboration, and keyword networks. The results show a significant growth of publications in the last five years, with major themes focusing on sustainable development, environmental technology, and economic implications of green innovation, while leading contributions come from China. Influential journals in innovation and environmental management are identified as key publication outlets, and keyword analysis reveals the integration of green innovation into sustainability strategies and economic policy discussions. This study contributes to a clearer understanding of the intellectual structure and emerging directions of green innovation research, offering insights for scholars, business practitioners, and policymakers in advancing sustainable innovation practices.

Employee welfare is an important element in the success and sustainability of an organization. This study aims to analyze the influence of leadership style, communication, and organizational culture on employee welfare at PT Bintang Teknik Nyata Sukodono, Sidoarjo. Leadership style emphasizes the leader's ability to create a clear and strategic vision of the future to empower employees. Communication in an organization includes the effectiveness of information delivery, openness, and two-way communication between leaders and employees. Meanwhile, organizational culture includes shared values, behavioral norms, and adaptation to change that shape interaction patterns within the organization. This study uses a quantitative approach with a survey method, involving all employees of PT Bintang Teknik Nyata as respondents. Data were collected through a questionnaire designed to measure indicators of leadership style, communication, organizational culture, and employee welfare. The analysis was carried out using multiple linear regression to identify the partial and simultaneous effects of independent variables on employee welfareThis study is expected to provide theoretical and practical insights. Theoretically, the results of this study contribute to the development of human resource management science, especially related to leadership style, communication, and organizational culture. Practically, this study can be a reference for PT Bintang Teknik Nyata in formulating strategies to improve employee welfare through leadership optimization, communication improvement, and the formation of a supportive organizational culture.

Digital payment systems have become one of the main innovations in financial transformation. Over the past few years, these systems have gained significant traction and are now at the forefront of reshaping financial landscapes globally. Currently, digital payment systems have changed global transactions by slowly replacing the transaction patterns of societies that were previously dominated by conventional transactions, offering more efficiency, security, and accessibility. This transformation is closely related to the development of fintech, which has given rise to instruments in the form of electronic money and blockchain technology. These advancements have not only changed the way payments are made but also enabled the inclusion of previously underserved populations in the financial ecosystem. This study uses a bibliometric approach to analyze scientific publications, with the main sources coming from Scopus using the keywords “digital payment systems,” “electronic money,” and “fintech.” By utilizing Biblioshiny in the VOSviewer application, this study aims to examine publication trends, contributions from various countries, institutions involved, and thematic connections between topics. In conclusion, this study contributes to expanding the understanding of the development of digital payment systems, while also presenting a global research map that can be used as a reference for academics, researchers, and policymakers involved in financial innovation.

Diseases in primary health services exhibit complex spatial-temporal dynamics due to urbanization and population mobility. Conventional surveillance approaches are difficult to capture these patterns adaptively. Machine learning (ML) based on spatio-temporal modeling offers a solution with the ability to detect disease clusters automatically and with high precision. Research Objectives: This research aims to develop a machine learning model to detect disease hotspots from primary service data in Indonesia, with a focus on improving prediction accuracy, interpretability, and relevance of health policies. Methodology: The primary service dataset for 2024 (5,343 entries) was analyzed using three ML models Gradient Boosting Machine (GBM), Temporal Random Forest (TRF), and Multi-EigenSpot with spatial (village) and temporal (week, month) features. Performance evaluation includes predictive (AUC, F1-score) and spatial (Moran's I, Spatio-Temporal Correlation Index) metrics. Results: The results showed that Multi-EigenSpot achieved the best performance (AUC=0.91; F1=0.86), with the detection of dominant hotspots in Sungai Asam and Beringin Villages. Moran's I value of 0.63 indicates a strong spatial autocorrelation, while STCI=0.57 indicates moderate temporal stability. Conclusions: ML-based spatio-temporal models are effective in identifying hidden disease patterns and have the potential to be integrated into national digital surveillance systems. This approach supports precision public health by providing a scientific basis for real-time location- and time-based intervention policies.

This study discusses the introduction of digital signature patterns using the Backpropagation method on Artificial Neural Network (JST) to identify a person's characteristics and potential. The increasing use of digital identities demands a verification system that is more secure, accurate, and adaptive to the variations of each individual's signature. The main problem faced in the signature recognition system is the low level of accuracy when the visual features of the signature have similarities between users, both in terms of shape, size, and stroke pressure. In addition, variations of signatures made by the same individual are also a challenge in the identification process. As a solution, this study implements Principal Component Analysis (PCA) to extract important features from the signature image before the training process using JST. PCA is used to reduce the data dimension so that the learning process becomes more efficient and optimal. A total of 80 signature images were used in this study, consisting of 60 training data and 20 test data. The results showed that the system was able to achieve an accuracy level of 92.5%. These findings prove that the combination of PCA and JST methods is effective in recognizing digital signature patterns and has the potential to be applied to digital security-based biometric identification systems.

William Shakespeare’s drama Antony and Cleopatra illustrates various aspects of hedonism, especially in the pursuit of love, power, and desire. These behaviors are reflected in the way the characters prioritize personal pleasure over political duty, showing how immediate gratification often clashes with responsibility. This study aims to explore the different types of hedonism represented in the play. A qualitative descriptive approach is used to analyze the dialogues and actions of the main characters, Mark Antony and Cleopatra, based on Weijers’ (2012) framework. The results reveal six categories of hedonism: folk hedonism (30%), value/prudential hedonism (20%), motivational hedonism (15%), normative hedonism (12.5%), utilitarian hedonism (12.5%), and egoist hedonism (10%). Among these, folk hedonism is the most dominant, particularly in Antony and Cleopatra’s pursuit of love and passion without considering the consequences for their political roles. The research shows that Antony and Cleopatra reflects how the pursuit of personal pleasure can shape character development, create conflict, and even lead to tragedy, illustrating timeless patterns where short-term desires overshadow long-term responsibilities.

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.  

Tourism is one of the sectors that plays an important role in boosting economic growth through travel activities and destination exploration. Tourists' preferences for nature-based tourism options, such as mountain hiking or beach tourism, are influenced by various factors, ranging from personal experiences and recreational interests to social characteristics. Therefore, a technology-based approach is needed to predict destination choice tendencies more accurately. As artificial intelligence technology develops, deep learning methods have been widely used in classification processes due to their ability to process large amounts of data and recognize complex patterns. In this study, a Multilayer Perceptron (MLP) model is used to classify tourists' preferences between mountain or beach destinations based on a survey dataset. The research stages include data processing, data splitting using a train-test split, model training, and performance evaluation using accuracy, precision, recall, and F1-score. The test results show that the MLP model is capable of achieving an accuracy rate of 99%, confirming that deep learning methods are effective in automatically mapping tourism preference trends. This research is expected to serve as a basis for the development of more personalized travel destination recommendation systems, as well as to support tourism management in formulating targeted promotional strategies.

The development of e-commerce and social commerce has driven significant changes in student shopping behaviour, including at FEB UNDARIS. Ease of access, digital promotions, and the influence of social media have led students to shop online more frequently, even impulsively. This study aims to analyse how attitudes, subjective norms, and perceived behavioural control interact in shaping students' online shopping behaviour using the Theory of Planned Behaviour (TPB) framework. This study uses a descriptive qualitative method with in-depth interviews with ten students who actively shop online. The data were analysed to understand the experiences, perceptions, and social factors that influence their decisions in digital transactions. The results show that positive attitudes towards efficiency and promotions, social pressure from friends and influencers, and high perceived behavioural control due to technological convenience work simultaneously to reinforce online shopping intentions and actions. These three TPB components support each other and trigger increasingly impulsive consumption patterns in the digital environment.

Artificial intelligence (AI) is currently a rapidly developing technology in all fields, particularly in finance and the military. This study aims to examine the application of Artificial Intelligence (AI) technology to support financial report analysis and internal control within Indonesian Navy (TNI AL) work units. Along with the development of information technology, AI has the potential to provide innovative solutions to improve efficiency, accuracy, and transparency in state financial management, particularly in a military environment that demands high accountability. The research method used was descriptive qualitative with a case study approach in several work units within the Indonesian Navy. Data were obtained through interviews, observations, and a review of relevant documents and literature. The results indicate that the use of AI, such as machine learning and data analytics, can identify unusual financial transaction patterns, predict potential irregularities, and improve the effectiveness of internal oversight. However, the implementation of this technology still faces challenges, such as limited digital infrastructure, the need for human resource training, and the need for policies that support sustainable digital transformation. This study recommends the gradual and strategic integration of AI as part of the reform of the Indonesian Navy's financial management system.

Inclusive education in early childhood settings emphasizes equal learning opportunities for children with special needs within regular classrooms. However, the implementation of inclusive practices in public kindergarten classrooms presents various challenges for teachers, particularly due to diverse learner needs and limited institutional support. This study aims to explore the challenges faced by teachers in teaching children with special needs in public kindergarten classrooms. Employing a qualitative descriptive research design, the study was conducted in a public kindergarten that implements inclusive education. The participants were kindergarten teachers selected through purposive sampling based on their direct experience teaching children with special needs. Data were collected through semi-structured interviews and classroom observations, and were analyzed thematically to identify recurring patterns and issues. The findings indicate that teachers encounter significant challenges in managing children’s behavior and attention, implementing instructional adaptations without systematic planning, assessing inconsistent developmental progress, and coping with emotional stress. These challenges are further intensified by limited professional competence, lack of formal training in inclusive education, and minimal institutional support, including the absence of support teachers and specialist services. The study highlights the need for targeted professional development, structured instructional planning, and strengthened school support systems to ensure the effective and sustainable implementation of inclusive education in public kindergarten settings.

In today's digital era, recommendation systems have become an integral part of supporting consumer purchasing decisions, including in the food and beverage industry. This study aims to develop a product recommendation system for snacks and beverages using the item-based collaborative filtering method. This method was chosen due to its ability to handle large-scale user and product data, as well as its efficiency in providing relevant recommendations based on user consumption patterns. In this study, the system calculates the average user rating and implements   Cosine Similarity to measure the similarity between products, resulting in more accurate recommendations. The system also evaluates the accuracy of recommendations using the Mean Absolute Error (MAE) metric. Based on the results obtained, which is 0.285403 for the average error on 17 items, the developed recommendation system can improve consumers' shopping experience, help them find products that suit their tastes, and support the sales of snacks and beverages products in the market

The limited number of studies examining organizational communication patterns within technical government institutions a research gap, particularly in understanding how communication mechanisms are implemented by public relations units in technical based governmental organizations. This research aims to identify the communication patterns applied by the Public Relations Unit of the Geological Survey Center (PSG) in Bandung, as well as the supporting factors and challenges encountered in its implementations. The study employs a qualitative descriptive approach wih data collected through interviews, observations, and literature review. The findings reveal that the organizational communication pattern operates in multiple directions using both formal communication channels, such as official letters and scheduled meetings through the Nadine system, and informal channels, including face-to-face interactions and digital messaging through WhatsApp. Supporting factors in the communication process include leadership support, a well-defined organizational structure, a collaborative work culture, and the availability of adequate communication facilities and media. However, several constraints remain, including technical and non-tehcnical challenges such as varying work priorities, inconsistents responses times, and unstable network connectivity during field activities.