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The development of digital technology has influenced children's communication and social interaction patterns, including at the elementary school level. One widely used platform is TikTok, which provides short video content with a combination of visual and verbal communication. This study focuses on analyzing the influence of TikTok content on the communication and self-expression patterns of elementary school students at MIM Ngadirejan. The research used a descriptive qualitative approach with a case study method. Data were collected through in-depth interviews with students and teachers, observations of student communication behavior in the school environment, and analysis of TikTok video content frequently watched by students. Data analysis was conducted using the Miles and Huberman model, which includes data reduction, data presentation, and conclusion drawing. The results show that consuming TikTok content influences students' use of informal language, adoption of popular terms, and imitation of non-verbal expressions. Furthermore, TikTok use also increases students' creativity and courage in expressing themselves, requiring parental guidance and strengthening digital literacy.

The rapid development of information technology has provided significant opportunities to improve the efficiency of academic administration in schools. One of the common problems faced by educational institutions is the manual process of subject scheduling, which is time-consuming and prone to schedule conflicts among teachers, classes, and learning time. This problem is also experienced by SMPK Flos Carmeli, where the preparation of subject schedules has not yet been supported by an integrated computerized system. This study aims to design and develop a web-based subject scheduling application at SMPK Flos Carmeli using the Model–View–Controller (MVC) architecture. The research method employed is Research and Development (R&D) with the Waterfall software development model, which includes the stages of requirements analysis, system design, and application implementation. Data were collected through interviews, observations, and literature review to obtain system requirements that align with the school’s conditions. The application was developed using native PHP with the implementation of the MVC pattern to produce a structured, maintainable, and flexible system. The results show that the developed application is able to support the subject scheduling process in a faster, more accurate, and well-organized manner. The system provides features for managing teacher data, class data, time slots, schedule arrangement, and schedule printing, thereby minimizing schedule conflicts and improving the efficiency of school administrative work. Therefore, this subject scheduling application is expected to support the digitalization of academic administration and enhance the effectiveness of the teaching and learning process at SMPK Flos Carmeli.

This study examines the integration of maqasid al-shariah and digital governance in halal value chains using a Systematic Literature Review (SLR) guided by the PRISMA 2020 framework. Articles indexed in Scopus from 2020–2025 were analyzed to identify theoretical patterns and research gaps. The findings indicate that existing studies remain fragmented, with maqasid primarily treated as a normative concept and digital governance addressed mainly from technological or compliance perspectives. This research proposes an integrative conceptual model linking maqasid al-shariah, digital governance, stakeholder engagement, institutional legitimacy, strategic resources, and sustainability outcomes. The synthesis demonstrates that maqasid can function as a strategic governance framework, while digital governance operationalizes Islamic values through transparent and measurable practices. The study contributes theoretically by repositioning maqasid as a strategic management lens and practically by offering guidance for value-based digital transformation in the halal ecosystem. Overall, maqasid-based digital governance emerges as a new paradigm for building ethical, resilient, and sustainable halal value chains.

Personal financial management behavior has gained prominence amid economic globalization, digital transformation, and crisis-induced shifts that reshape individual decision-making, budgeting, saving, and risk practices. This study conducts a comprehensive bibliometric analysis to chart the intellectual structure, growth patterns, and future orientations of research in this domain. Drawing on 312 English-language publications from the Scopus database spanning 2000 to 2024, the analysis employs VOSviewer for co-authorship, keyword co-occurrence, and co-citation mapping, complemented by performance metrics on trends and productivity. Findings reveal a marked acceleration in scholarly output, particularly after 2020, driven by heightened attention to digital tools and resilience factors. Thematic clusters highlight progression from foundational literacy and demographic influences to psychological mediators (e.g., self-efficacy, attitudes) and outcomes centered on well-being and socialization. Geographic contributions concentrate in the United States and Indonesia, with strong Asia-Pacific networks, while productive authors form specialized collaborative hubs. The intellectual base integrates behavioral frameworks with empirical applications, underscoring interdisciplinary depth. These insights address fragmentation in prior work by providing a unified knowledge map, revealing gaps in cross-cultural integration and dynamic digital modeling. Implications extend to guiding targeted interventions for financial education and policy, fostering individual resilience in volatile environments. This synthesis supports scholars and practitioners in advancing evidence-based approaches to sustainable personal finance practices.

The phenomenon of Fear of Missing Out (FoMO) has emerged as a significant psychosocial factor influencing the consumption behavior of Generation Z in the social media era. This study aims to explore how FoMO shapes the purchasing decisions of Generation Z using a qualitative approach, focusing on subjective experiences, digital social pressures, and interactions with viral content. A descriptive-interpretative qualitative method was employed, collecting data through in-depth interviews and observations of social media activity among Generation Z respondents actively engaged in digital platforms. Thematic analysis was applied to identify patterns of FoMO experiences and their impact on purchasing decisions. Findings reveal that FoMO arises from the need for social connection, social comparison with peers and influencers, and intensive exposure to digital trends. This phenomenon drives consumptive behaviors, particularly impulsive purchases, influenced by social validation and emotional impulses. Generation Z’s purchasing decisions are contextual, multidimensional, and not entirely rational, yet meaningful within the framework of digital social identity. The study underscores the necessity of a qualitative approach to comprehensively understand the psychological, social, and cultural complexities of FoMO and consumer behavior. The results provide a conceptual foundation for ethical and relevant marketing strategies targeting young consumers in the digital era.  

The Baduy tribe is an indigenous community in Banten Province divided into two main groups, namely Inner Baduy and Outer Baduy, with varying levels of adherence to customary law and exposure to modernization. This article examines various government policies that affect the survival and identity of the Baduy tribe. These policies include the recognition of customary rights through Lebak Regency Regulation No. 32 of 2001, the designation of Kanekes Village as customary land, and the designation of the Baduy area as a tourist destination. Although these policies aim to protect the rights and preserve the culture of the Baduy, their implementation has created a dilemma. On the one hand, tourism opens up new economic opportunities and raises cultural awareness. On the other hand, the influx of tourists triggers significant social, cultural, economic, and environmental changes, especially among the Outer Baduy, including the adoption of modern technology, shifts in consumption patterns, and increased environmental pollution. Thus, preservation policies have ironically become drivers of change that undermine the Baduy community's original identity.

This study examines the stigma and patterns of social support in adolescents with unplanned pregnancies in Makassar in the context of the Bugis-Makassar culture that upholds the values of siri' (honor and shame). Teen pregnancy is understood as an experience influenced by the confluence of traditional values, religious morality, and modern social demands. This study used an Interpretative Phenomenological Analysis (IPA) approach on 17 adolescent girls aged 15–19 years who experienced unplanned pregnancies in June–November 2023. Data were obtained through in-depth semi-structured interviews, recorded, verbatim transcribed, and analyzed using a six-stage science process with a focus on the experience of stigma and social support. Two main themes emerged: Living Under the Shadow of Siri': Multidimensional Stigma and Navigating Social Support in the Context of Shame. Participants experienced widespread stigma in a variety of contexts, including family (serial violations and family shame), society (moral judgments and gossip), health services (discrimination of health workers), and educational environments (bullying and exclusion). Stigma is also internalized in the form of guilt and negative identity, and is especially severe in adolescents with economic limitations. Efforts to seek support are often hampered by concerns about stigma, so adolescents engage in selective disclosure and rely more on peers. Grandma is often the most accepting source of family support. These findings underscore the need for multilevel interventions that reduce community stigma, strengthen friendly and confidential health services, and develop culturally sensitive peer support networks.

Artistic creativity is an essential aspect of child development, contributing to imagination, fine motor skills, and self-confidence. However, limited access to art-based activities and excessive gadget use have reduced opportunities for children to develop creativity, particularly in rural areas such as Dogang Village, Langkat Regency. This community service program aimed to enhance children’s artistic creativity through the socialization of bracelet-making using paracord as a form of simple and practical non-formal education. The study employed a descriptive qualitative approach, with data collected through observation, interviews, and documentation involving children who participated in the activity. The socialization process included the introduction of tools and materials, demonstrations of basic knotting techniques, and hands-on practice with direct assistance. The results indicated that participants were able to master several basic techniques, including simple knots, braided patterns, and double (cobra) knots, which contributed to improved creativity, precision, social interaction, and self-confidence. In addition to its educational impact, the bracelets produced demonstrated economic potential, with an affordable selling price starting from Rp.5,000 per unit. Therefore, this activity not only supports the development of children’s artistic creativity but also provides opportunities for sustainable micro-enterprise development within the local community.

The advancement of information technology has rapidly transformed trading patterns in Indonesia, shifting from conventional transactions to online transactions through marketplace platforms. On one hand, this transformation provides convenience and efficiency for both businesses and consumers. On the other hand, it has also given rise to various legal issues, particularly regarding consumer protection. This article aims to examine how legal protection for consumers is implemented in electronic sales agreements on marketplaces, while also identifying the obstacles encountered during its implementation. The study employs a normative juridical approach, using conceptual analysis and legislative review, supplemented by empirical data obtained from interviews. As described, legal protection for consumers in electronic transactions in Indonesia remains suboptimal. Specifically, these challenges include biased law enforcement, low levels of consumer literacy, and ineffective dispute resolution mechanisms. In practice, marketplaces have incorporated consumer protection features such as escrow systems, refund mechanisms, and complaint centers; however, their implementation still suffers from limited transparency and effectiveness. Furthermore, existing regulations are slow to respond to the dynamics of cross-border transactions and ongoing digital innovations. Therefore, comprehensive regulatory reform, stronger enforcement, and enhanced legal and digital literacy among the public are necessary to ensure effective consumer protection.

Communication in digital media, especially YouTube, now plays an important role as a means of delivering educational material to the wider community. Nevertheless, studies on the phenomenon of communication on this platform, especially from the perspective of locutionary acts, are still limited. Philosophy-themed content, which is often considered abstract and complex, requires linguistic analysis to understand effective communication patterns. This study aims to identify and describe various forms of locutionary acts that appear in the “Belajar Filsafat” (Learning Philosophy) playlist on the YouTube channel Satu Persen – Indonesian Life School. This study was conducted using a qualitative descriptive method based on pragmatic theory. Data were collected through observation and note-taking techniques in four videos from the playlist, then analyzed using matching techniques and presented informally. The results of the study show that there are 151 locutionary acts, including 76 declarative acts, 46 interrogative acts, and 29 imperative acts. Theoretically, this study contributes to the development of linguistic studies, particularly in the field of pragmatics that discusses speech acts, while practically, this study can be a reference for students and novice researchers in understanding speech act research methods and the importance of applying effective communication in the realm of digital education.

Increasingly complex environmental problems, including pollution, ecosystem degradation, and climate change, demand greater awareness and active participation from all sectors of society, particularly students as agents of change. A clean and green campus environment is essential for supporting comfort, health, and the sustainability of academic activities. As centers of education and innovation, universities hold a strategic responsibility to cultivate environmental awareness through the implementation of the green campus concept. This study aims to examine the level of student awareness regarding campus cleanliness and greening based on findings from previous research. The method employed is a literature review, analyzing various relevant sources such as scientific journal articles, academic books, and policy documents related to sustainable campus management. The analysis was conducted using a descriptive qualitative approach to identify recurring patterns, prevailing trends, and key factors influencing students’ environmental awareness and behavior. The results indicate that student awareness levels generally fall within the fair to good category. Students with higher environmental awareness tend to participate more actively in maintaining cleanliness and supporting campus greening initiatives. However, the development of environmentally responsible behavior is strongly influenced by adequate facilities, supportive institutional policies, and a sustainability-oriented campus culture. Therefore, strengthening the role of higher education institutions through comprehensive environmental policies and structured sustainability programs is crucial to fostering clean, green, and sustainable campuses.

Diabetes Mellitus is one of the chronic diseases with high morbidity and mortality rates, making data-driven analysis necessary to understand patient mortality patterns. This study aims to analyze the mortality rate of Diabetes Mellitus patients based on age and length of hospitalization using a data mining approach with the K-Means Clustering method. The study employs a quantitative approach using secondary data obtained from the medical records of Diabetes Mellitus patients at Ibnu Sina Regional General Hospital, Gresik Regency, in December 2022. The dataset consists of 266 patient records with variables including age, length of stay, and final patient status. Data analysis was conducted through preprocessing stages, including data cleaning, transformation, and normalization, followed by the clustering process using the K-Means algorithm with the assistance of the RapidMiner application. The results show that patient data are divided into three clusters based on age ranges: 0–40 years, 41–55 years, and 56–90 years. The cluster with the age range of 56–90 years has the highest number of patient deaths compared to the other clusters. Meanwhile, the length of hospitalization does not show a significant effect on patient mortality. This study is expected to serve as a consideration for hospitals and health institutions in efforts to prevent and manage Diabetes Mellitus, particularly among the elderly population.

Learning concentration is an important factor in supporting students’ academic success, particularly in Biology study programs that require high levels of accuracy and analytical skills. Learning concentration is influenced by various factors, including sleep quality and dietary patterns. This study aimed to analyze the relationship between sleep quality and dietary patterns and the level of learning concentration among Biology students of class T.BIO1/2022 at Universitas Islam Negeri Sumatera Utara. This study employed an analytic observational approach with a cross sectional design. The research sample consisted of 23 students selected using a total sampling technique. The research instruments included questionnaires on sleep quality, dietary patterns, and learning concentration. Data analysis was conducted descriptively and using cross tabulation. The results showed that students with good sleep quality and regular dietary patterns tended to have higher levels of learning concentration compared to those with poor sleep quality and irregular dietary patterns. Therefore, sleep quality and dietary patterns are important factors that need to be considered to support students’ learning concentration.  

Rainfall is a crucial factor in the stability of the Earth's ecosystem and has a significant impact on agriculture, forestry, energy, and water management. However, increasingly unstable climate change makes rainfall patterns difficult to predict accurately using traditional methods. The city of Medan, the capital of North Sumatra Province, has a tropical rainforest climate with an average annual rainfall of approximately ±2200 mm and an average temperature of 27°C. Significant weather fluctuations in this area can trigger flooding when rainfall increases and cause water shortages when rainfall decreases (BMKG, 2021). Therefore, a prediction approach that can manage non-linear and dynamic data is needed. Artificial Neural Networks (ANN) are one of the reliable machine learning methods for detecting data patterns. By using the backpropagation algorithm, the model can gradually reduce prediction errors, making it widely used in weather forecasting applications. In this regard, this study uses ANN with the backpropagation method to forecast monthly rainfall in Medan City by utilizing data from 2022–2024 as training and testing data.

College represents a critical transitional period requiring high adaptation, where students often face escalating academic demands triggering psychological pressure. This study aims to deeply analyze the reciprocal relationship between sleep patterns and academic stress levels, and identify their subsequent impacts on cognitive function (concentration) and students' physical health. This study employs a qualitative method with a Systematic Literature Review approach. Data were collected through comprehensive searches of accredited national and reputable international scientific articles published within the last five years (2020–2025). Literature selection was conducted rigorously using specific keywords to screen empirical studies relevant to stress, sleep, and student health variables. Data synthesis reveals a strong and consistent negative correlation. High levels of academic stress whether stemming from the adaptation phase of new students (initiation), thesis preparation workload, or role conflicts in working students are significantly associated with decreased sleep quality. Physiologically, stress triggers increased cortisol levels causing insomnia, shortened sleep duration, and prolonged sleep latency. Subsequent impacts are proven to drastically reduce learning concentration and trigger physical health disorders, including an increased risk of primary dysmenorrhea in female students. This study concludes that academic stress and sleep disturbances form a destructive cycle that exacerbates one another. Therefore, holistic institutional interventions in the form of sleep hygiene education and stress management training are needed to break this chain for the sake of student well-being and academic performance.

Male infertility represents a major global health concern, with oxidative stress playing a central role in the deterioration of sperm quality. In recent years, growing attention has been directed toward herbal medicine as a potential alternative or complementary therapy due to its antioxidant and multi-target properties. However, a comprehensive long-term overview of the global research landscape in this field remains limited. A bibliometric and visualized analysis was conducted using publications indexed in the Scopus database from 2005 to 2025. Eligible peer-reviewed articles were retrieved following PRISMA guidelines. Bibliometric indicators were analyzed using Biblioshiny (R-Bibliometrix) to assess publication trends, leading contributors, and thematic evolution, while VOSviewer was employed to visualize international collaboration networks, co-citation patterns, and keyword co-occurrence clusters. A total of 562 articles were included, revealing a strong upward trend in scientific output, particularly after 2016, with an annual growth rate of 15.78% and a peak in 2025. Iran, China, and Nigeria emerged as the most productive countries, while the Faculty of Veterinary Medicine and several universities in Africa, the Middle East, and Asia were identified as leading institutions. Keyword analysis highlighted three dominant research fronts: semen quality assessment, testicular histopathology, and oxidative stress–related molecular mechanisms. The collaboration network indicated increasing global and South–South research synergy. The field of herbal medicine for male infertility has evolved into a mature and multidisciplinary research domain, increasingly grounded in mechanistic and experimental evidence. These findings provide a strategic overview of research hotspots and emerging trends, offering valuable guidance for future experimental and clinical investigations.

In demographic studies, population growth refers to changes in the number of inhabitants over a given period, measured by calculating numerical differences in population size and expressing them within specific time units to illustrate patterns of increase or decline. Based on data from Tembalang District in Figures 2025, the population growth rate in Kedungmundu Village rose by 0.51% between 2016 and 2025, with most residents relying on clean water services provided by PDAM Tirta Moedal as their primary source of drinking water. This study aims to estimate clean water demand based on projected population growth and to design a pipeline network system capable of meeting future needs. The analysis of water demand applies population projection methods, including arithmetic, geometric, and least square approaches, to compare their levels of accuracy, while the clean water distribution network is modeled using EPANET 2.0. One of the main challenges faced by PDAM is that service coverage has not yet been fully optimized. The ten-year projection results indicate that the arithmetic method provides a correlation value closest to 1, estimating a future population of 14,904 people with a total clean water requirement of 3.48 liters per second. To support this projected demand, the proposed network design utilizes High Density Polyethylene (HDPE) pipes with diameters of 12, 10, and 8 inches to ensure efficient and sustainable water distribution.

Savings and Loan Cooperatives (KSP) play a vital role in expanding community access to capital, especially within the informal sector. Nevertheless, non-performing loans remain a persistent challenge that can threaten liquidity and long-term institutional sustainability. KSP CU Mera Ndi Ate faces similar issues, which are assumed to stem not only from administrative weaknesses but also from members’ perceptions and behavioral factors. This research aims to examine the potential causes of non-performing loans through text-based sentiment analysis using an unsupervised learning approach. A quantitative method with a data mining framework was applied. Data were gathered through interviews, observations, documentation, and 200 customer opinion texts processed using the Orange Data Mining application. The analytical stages included preprocessing, corpus development, feature extraction, sentiment clustering, and visualization. Because the dataset lacked predefined labels, unsupervised learning was used to identify naturally emerging sentiment patterns. Findings reveal a predominance of critical sentiments related to credit assessment procedures and service quality. The highest sentiment score (75) concerned insufficient creditworthiness evaluation, followed by concerns about service efficiency (66.6667). These insights suggest that improving assessment accuracy and service quality may help reduce non-performing loans.

In the rapidly evolving digital economy, the ability to anticipate transaction surges is a strategic asset for marketplace platforms to maintain operational efficiency. This research aims to build an accurate daily transaction volume forecasting system thru the implementation of an Extract, Transform, and Load (ETL) pipeline and Autoregressive Integrated Moving Average (ARIMA) predictive modeling. The dataset used is sourced from dataset_olshop.csv, which includes transaction history for the entire year of 2025. The ETL stage focused on data cleaning and handling missing values, while time series analysis began with the Augmented Dickey-Fuller (ADF) stationarity test, which yielded a significant p-value of 0.000006. The parameter model was optimized using the auto_arima algorithm, which determined the ARIMA(2,0,0) configuration as the best model. The evaluation results of the model show fairly stable performance with a Root Mean Squared Error (RMSE) value of 2.002 and a Mean Absolute Error (MAE) of 1.704 on the test data. Research findings reveal a consistently higher purchasing pattern during the mid-month and end-of-month periods, with an average of 5.52 daily transactions, compared to the beginning of the month, which saw 5.48 transactions. The 30-day forecast results provide valuable insights for online store managers to proactively adjust inventory and logistics workforce allocation strategies. This research concludes that integrating data engineering techniques and statistical analysis can provide predictive solutions for the dynamics of the digital market.

This study is motivated by the increasing need for accurate modeling and classification of one-dimensional signal data in intelligent systems. The rapid development of deep learning has led to the adoption of more adaptive and complex neural network architectures capable of capturing both temporal dependencies and local patterns in sequential data. This research aims to analyze and compare the performance of several deep learning models, namely Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and a hybrid Convolutional Neural Network–GRU (CNN–GRU) model for signal data classification. The research method employs a quantitative experimental approach involving data preprocessing, windowing, model training, and performance evaluation. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. The results indicate that the hybrid CNN–GRU model outperforms the other models, particularly in capturing local features and long-term temporal dependencies within signal data. These findings suggest that the integration of convolutional layers and recurrent mechanisms enhances feature representation and learning stability. This study is expected to contribute both theoretically and practically to the development of deep learning models for signal processing and time-series-based intelligent applications.