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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 study investigates Qur’an-based healing practices within the Jakarta Qur’anic Structure Studies Community, focusing on the understanding of the Qur’an as Asy-Syifā’ (a source of healing) and the therapeutic application of Qur’anic verses through the Qur’anic Structure method. Employing a qualitative, descriptive-analytical design within a Living Qur’an and phenomenological framework, data were collected through participant observation, in-depth interviews with three key informants, and questionnaires administered to 23 practitioners. The findings show that the community positions the Qur’anic Structure method as an alternative approach to both physical and spiritual healing by treating the entire Qur’an as a medium of cure. This practice is operationalized through four principal patterns: the ‘Ain Structure, Page Structure, Alphabet Structure, and Surah Structure, all of which refer to the 18-line mushaf published by PT Taj Company (Karachi, Pakistan). Practitioners’ testimonies indicate perceived improvements in physical and psychological conditions, framing this method as a form of Qur’anic self-healing (ikhtiar) while reaffirming that ultimate healing belongs solely to Allah SWT.

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.

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.

Customer satisfaction has become a major focus in the development of modern services as the role of the service sector in the regional economy increases, including in the city of Balikpapan. However, the literature discussing customer satisfaction models shows conceptual and methodological fragmentation, requiring a systematic synthesis to identify theoretical patterns and current research developments. This study aims to map customer satisfaction models in the service industry through a Systematic Literature Review (SLR) approach using the PRISMA guidelines. A total of 20 articles selected from the last five years were analyzed to identify dominant constructs, methodological approaches, and relevant research trends. The review results show that customer satisfaction models have evolved from traditional service quality-based approaches to models that integrate customer experience, perceived value, trust, and digital dimensions such as personalization and AI-based services. The findings also emphasize the importance of local context, especially in the service sector in Balikpapan, where cultural factors, customer behavior, and the dynamics of city growth influence the structure of satisfaction models. Overall, this study provides a synthesis framework that can be used as a theoretical and practical foundation for designing service quality improvement strategies and developing a further research agenda in the service sector.

This study aims to analyze hematocrit values ​​using a micro method with capillary blood samples in pregnant women with anemia at the Sidodadi Kisaran Barat Community Health Center. This study is a descriptive study with a cross-sectional approach involving 15 pregnant women as respondents. Data collection was carried out through hematocrit laboratory examinations and data collection of respondent characteristics. The results showed that 60% of respondents had hematocrit levels below normal values, while 40% of respondents had hematocrit levels within normal limits. Low hematocrit levels in pregnant women are influenced by several factors, including age, education level, employment status, and level of knowledge about anemia during pregnancy. This condition has the potential to increase the risk of pregnancy complications if not optimally managed. Therefore, it is recommended that health workers continue to increase educational efforts regarding anemia prevention, the importance of regular pregnancy check-ups, and encourage pregnant women to maintain a balanced nutritional consumption pattern and adequate iron intake to support maternal and fetal health.

This study examines the role of mathematics learning in improving logical thinking skills in early childhood. The background of this study is based on the importance of logical thinking skills as a foundation for children's cognitive development, which can begin at an early age through appropriate mathematics learning. The purpose of this study is to analyze how mathematics learning can stimulate the development of logical thinking in early childhood and explore effective learning strategies. The methods used are library research and observation of several models of mathematics learning for early childhood in early childhood education institutions. The findings indicate that fun and concrete activity-based mathematics learning can improve children's abilities in critical thinking, constructing patterns, drawing conclusions, and solving simple problems. The implications of this study emphasize the need for the application of creative and interactive mathematics learning methods to support the development of logical thinking from an early age, while also encouraging educators to integrate mathematics into children's daily activities. This study also recommends the development of learning media appropriate to children's developmental stages for optimal results.

The transformation of the post-pandemic education system has crystallized online learning as a permanent component, with parents assuming an unprecedented instructional role at the elementary level, particularly at Madrasah Ibtidaiyah (MI). This qualitative case study aims to analyze in depth the dynamics and implications of the role of these parents during the transition period. Data was collected through in-depth interviews with parents, students, and teachers from two MIs in Blora, enriched by observation and analysis of communication documents. The findings of the study reveal three crucial things: first, there is a transformation of the role of parents into "substitute teachers" which creates a significant psychological burden due to the gap between the demands of the role and pedagogical capacity. Second, the communication pattern between schools and parents is still one-way and administrative, and has not built a collaborative three-way partnership by involving students. Third, the quality of parental assistance is very diverse and highly dependent on their digital literacy capacity and educational background, which directly affects the development of students' self-regulated learning. This study concludes that the effectiveness of parental roles is determined by psycho-educational support, the quality of communication channels, and the level of personal capacity, so policy implications must move towards a differentiated and empowerment-oriented approach.

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.

Agrarian conflicts in Lampung illustrate that land disputes are not only about ownership but also about how language shapes public perceptions of power and justice. This study aims to explain the changing patterns of media reporting on agrarian conflicts and the social meanings emerging from these changes. The research uses a descriptive qualitative approach with Fairclough’s critical discourse analysis and Lederach’s conflict transformation theory. Data were obtained from online news reports on agrarian conflicts in Lampung published between 2010 and 2025. The results show that earlier reports emphasized control and security, while later coverage shifted toward issues of justice, land rights, and community recognition. This change indicates that language in media reporting plays a crucial role in transforming public perspectives from a logic of authority toward a consciousness of justice and humanity. The study highlights the importance of fair and empathetic public communication as a foundation for peaceful and sustainable conflict resolution.

The dynamic changes in weather patterns in Jambi City require an accurate temperature prediction system, thus this study aims to compare the performance of Random Forest and Support Vector Regression (SVR) algorithms in predicting daily maximum temperatures using weather data from 2020–2024 obtained from OpenMeteo with the application of Feature Engineering including lag and rolling window features. The test results indicate that the SVR model with a Radial Basis Function (RBF) kernel optimized using Grid Search (C=10, epsilon=0.2, gamma=0.01) significantly outperforms Random Forest based on a statistical Paired T-test (p-value < 0.05), yielding an R-squared (R²) value of 87.46%, Mean Absolute Error (MAE) of 0.3818 °C, and Root Mean Squared Error (RMSE) of 0.4964 °C compared to Random Forest's R² of 84.05%, where the previous day's temperature (lag) and three-day rolling average were identified as the most dominant predictors, leading to the recommendation of SVR as the more effective method for temperature prediction in the study area.

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.

This study aims to classify customer payment methods at 17 Coffee & Eatery using machine learning algorithms, namely Naïve Bayes and Support Vector Machine (SVM). The increasing use of digital and non-cash payments has generated large volumes of transaction data that are rarely analyzed optimally, even though such data contain valuable information for business decision making. This research used secondary transaction data collected from January to March 2025, consisting of 10,147 transaction records. The dataset included several attributes such as order time, payment time, transaction type, total sales, number of items, and payment method. Data preprocessing was performed through data cleaning, feature engineering, normalization, and label encoding before being divided into training and testing sets with an 80:20 ratio. The Naïve Bayes and SVM models were then trained and evaluated using accuracy, precision, recall, F1-score, and ROC–AUC metrics. The results show that both algorithms were able to classify payment methods effectively, but SVM achieved higher accuracy and more stable performance than Naïve Bayes. These findings indicate that SVM is more suitable for handling complex and heterogeneous transaction patterns. The implementation of machine learning for transaction classification can support more efficient financial management and data-driven decision making for small and medium enterprises in the culinary sector.

The Community Service (PkM) activity aimed to enhance the capacity of civil society to conduct effective public oversight of the East Lombok Regency Regional Revenue and Expenditure Budget (APBD) for fiscal year 2025. The “Budget School” program, organized by the Pimpinan Daerah Pemuda Muhammadiyah in collaboration with the Indonesian Forum for Budget Transparency (FITRA) NTB and ITSKes Muhammadiyah Selong, provided participants with a comprehensive and critical analysis of the regional budget structure and allocation patterns. The key findings highlighted notable fiscal inefficiencies, particularly the disproportionately high allocation for Employee Spending (Belanja Pegawai), which indicates an urgent need for budget reallocation toward increasing Capital Expenditure (Belanja Modal). Such realignment is essential to accelerate infrastructure development, enhance public service delivery, and ensure broader socio-economic benefits for the community. The event, conducted on September 25, 2025, successfully improved fiscal literacy among youth and civil society actors by strengthening their understanding of fiscal governance and legal oversight mechanisms. Overall, the activity fostered collective awareness and encouraged active participation in promoting sustainable, transparent, and efficient regional financial management.

Automatic motorcycle spare part sales have increased along with the high use of automatic two-wheeled vehicles in the community. To support optimal sales strategies and stock management, customer purchasing pattern analysis is required. This study uses the FP-Growth algorithm to identify association patterns between automatic motorcycle spare part products that are frequently purchased together. FP-Growth was chosen because of its ability to efficiently find frequent itemsets without the need to generate candidate itemsets as in the Apriori algorithm. Transaction data is processed to form an FP-Tree which is then extracted to find relationships between items. The analysis results show combinations of products that frequently appear together, such as brake pads and engine oil, which can be used as a basis for compiling sales packages, product placement, and product recommendations. By implementing the FP-Growth algorithm, spare part stores or workshops can improve service and efficiency in sales management.

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 demand for interactive web applications has encouraged the adoption of server-driven approaches such as Livewire as an alternative to building Single Page Applications (SPAs) without complex client-side JavaScript. However, the performance implications of this approach compared to conventional methods remain insufficiently explored. This study presents an empirical comparison between Laravel Blade with AJAX and Livewire in an academic attendance system scenario. Performance evaluation was conducted using k6 on the same web server, complemented by manual browser-based testing to observe actual communication patterns. The results indicate that Livewire exhibits approximately 2.7× higher average response time and up to 6× greater bandwidth consumption than Laravel Blade, primarily due to its snapshot mechanism and state synchronization process. Conversely, Livewire demonstrates better stability, reflected by lower maximum response times and a 0% error rate. These findings highlight a clear trade-off between resource efficiency and development convenience, where Livewire favors stability and developer productivity, while Laravel Blade provides superior efficiency in terms of latency and bandwidth usage.

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.

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.