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Dengue Hemorrhagic Fever (DHF) remains a major public health problem in Indonesia, particularly in densely populated areas. Control efforts require accurate data and spatial analysis to understand disease distribution patterns. Geographic Information System (GIS) is an effective tool for visualizing case distribution and supporting surveillance and planning of control programs at the primary healthcare level. This study aims to describe the spatial distribution of Dengue cases based on medical record data and produce a geographic distribution map to support Dengue control efforts at the Puskesmas level. This study used a quantitative descriptive design with secondary data from medical records at Karangsari Health Center. The sample consisted of 255 DHF patients in 2025, selected using a total sampling technique. Data were processed through editing, geocoding patient addresses, and spatial analysis using QGIS software.The results showed 255 Dengue  cases in 2025 with fluctuating monthly trends, peaking in April and lowest in December. Case distribution was uneven and tended to cluster. High-risk areas accounted for 15.7%–21.2%, moderate-risk areas 9.8%–15.7%, and low-risk areas 7.1%–9.8%. Megu Cilik Village had the highest proportion of cases, while other villages were categorized as moderate and low risk. This pattern indicates that Dengue incidence is influenced by environmental conditions, vector density, host factors, rainfall, and Aedes aegypti presence. GIS provides clearer spatial visualization, helping identify high-risk areas and supporting targeted public health interventions.

Health-related SMEs in Indonesia have significant opportunities to enter international markets, but many businesses still face limitations in digital capabilities, innovation, and readiness for external markets. This study aims to examine the influence of digital transformation on the success of health-related SMEs in going international, both directly and through business innovation as a mediator. The study employs a quantitative approach using a cross-sectional survey design involving 200 owners or key managers of SMEs in East Java, selected via purposive sampling. Data were analysed using Structural Equation Modelling-Partial Least Squares (SEM-PLS). The results indicate that digital transformation has a positive influence on business innovation and the success of going international. Business innovation also has a positive effect on internationalisation success and mediates the relationship between digital transformation and external market success. These findings confirm that digitalisation should not merely be understood as an operational tool, but as a strategic capability that must be integrated with product, process, marketing, and business model innovation. The research implications emphasise the importance of strengthening digital capabilities and business innovation to enhance the global competitiveness of Indonesian health-related SMEs

The coffee shop industry in Indonesia is experiencing rapid growth that requires business owners to optimize data-driven strategies. This study aims to analyze customer preferences at Semanis Coffee and Resto using data mining methods  to support more effective business decision-making. The method used is Market Basket Analysis with the FP-Growth algorithm for association rule mining and the K-Means algorithm for customer segmentation. The research data consists of 672 sales transactions during the March-May 2025 period. The results of the association analysis with a minimum support of 0.004 and a minimum confidence of 0.2 resulted in five valid rules with a lift ratio above 1. The strongest rule is the combination of Americano→Milk Choco with a confidence of 42.9% and an elevator ratio of 5.229, indicating a strong linkage between products. The most popular products are Milk Choco (10.8%) and Americano (8.5%). Customer segmentation analysis identified three clusters: Cluster 0 (Loyal Customers) 80% with high frequency but low transaction value; Cluster 1 (Occasional Customers) 10% with low activity; and Cluster 2 (Large Buyers) 10% with high transaction value but low frequency. This study concludes that product bundling strategies, loyalty programs, reactivation campaigns, and premium services can be applied to increase the effectiveness of coffee shop businesses.

Individual behavioral factors are critical determinants of organizational effectiveness and a vital component of modern organizational diagnosis. This study aims to identify and map individual behavioral factors as strategic indicators in organizational diagnosis using a Systematic Literature Review (SLR) approach guided by PRISMA 2020. Analysis of ten selected articles reveals that organizational effectiveness in the digital transformation era is driven by a reciprocal equilibrium between an individual's cognitive infrastructure and volatile work environment demands. The findings synthesize these behaviors into four strategic clusters: psychological well-being as primary infrastructure, digital structural support audits, justice and trust equilibrium, and psychological contract synchronization. This research provides tactical implications for Human Capital practitioners to transform annual diagnostic methodologies toward the implementation of monthly pulse surveys to detect fluctuations in well-being and disengagement intentions in real-time. This predictive diagnostic step is essential to mitigate turnover risks and design precise institutional interventions in hybrid work ecosystems.

Online games are one of the digital entertainment activities widely favored by adolescents; however, high-intensity use can lead to psychological impacts, one of which is anxiety. Anxiety in adolescents is characterized by feelings of restlessness, irritability, difficulty concentrating, and sleep disturbances, especially when they are unable to play online games or when they experience defeat in the game. This study aimed to determine the relationship between the intensity of online game use and the level of anxiety among adolescents. This study used a quantitative design with a cross-sectional approach, involving 113 respondents selected using a cluster sampling technique. The research instruments used were the DASS questionnaire and an online game usage intensity questionnaire, and the data were analyzed using univariate and bivariate analysis. The respondents in this study were adolescents from SMAN 1 Wanadadi, most of whom were 16 years old (49.6%) and predominantly female (61.9%). The intensity of online game use was mostly in the moderate category (55.8%), while the level of anxiety was mostly in the mild category (89.0%). The results showed that most respondents had a moderate level of online game usage intensity and a mild level of anxiety. Bivariate analysis indicated that there was no significant relationship between the intensity of online game use and the level of anxiety among adolescents, with a p-value of 0.425 and a contingency coefficient value of 0.076. Therefore, it can be concluded that there is no relationship between the intensity of online game use and the level of anxiety among adolescents.

Dengue fever is a disease commonly found in tropical and subtropical regions. This disease can cause severe symptoms, such as very high fever, accompanied by nausea, vomiting, headache, abdominal pain, and leukopenia (decrease in white blood cells). This infectious disease, known as dengue hemorrhagic fever (DHF), is a viral infection transmitted by the Aedes Aegyppti mosquito. This study aims to classify dengue-prone areas using the K-Means Algorithm, and to classify the factors that cause dengue in Purwodadi District, Grobogan Regency. The clustering results using the K-Means algorithm with Rapidminer tool from 266 data produced 3 clusters: cluster 0 (blue) with 138 patients dominated by Kuripan, Purwodadi, Ngambak villages, cluster 1 (green) with 31 patients in Ngraji, Nambuhan, Cingkrong villages, and cluster 2 (orange) with 97 patients in Danyang, Kalongan, Pulorejo villages. This study is expected to provide additional information for stakeholders in controlling dengue cases and increase awareness of the importance of environmental cleanliness as a preventive measure.

This study aims to identify domestic visitor segments in Bromo Tengger Semeru National Park (TNBTS), Indonesia, based on travel characteristics and consumption patterns to support the development of quality tourism in protected areas. Using snowball sampling, 283 domestic visitors was analysed by Two-Step Cluster Analysis in SPSS by integrating length of stay, activity preferences, and expenditure patterns. The results reveal a two-cluster solution as the most optimal segmentation, supported by the highest ratio of distance measures, with cluster quality rated as fair (silhouette = 0.20). Cluster 1 (39.2%) represents short-stay, lower-spending visitors who primarily seek iconic experiences (“Sunrise Seekers”), while Cluster 2 (60.8%) reflects longer-stay, higher-spending visitors who prefer village tourism activities (“Village Experience Seekers”). The strongest differentiating variables across segments are length of stay, activity preference, expenditure style, and age, whereas gender, education level, origin, and travel companions have limited role in segment separation. This study contributes empirical evidence of data-driven visitor segmentation in a conservation-based ecotourism destination within a volcanic national park, extending prior expenditure-focused profiling by integrating length of stay and activity preferences to capture visitor heterogeneity more comprehensively.

The tempe chip industry in Sanan Village, Malang City, represents a culturally embedded small and medium enterprise (SME) cluster that has contributed to the local economy for decades. However, intensifying intra-cluster competition, soybean price volatility, and shifting consumer behavior have placed increasing pressure on the competitiveness of businesses in this area. This study aims to analyze the marketing strategy implemented by Keripik Tempe Dua Karunia — one of the pioneering enterprises in Sanan Village that has been operating since 1980 — and to identify the marketing strategy factors that contribute to enhancing its competitive advantage. A qualitative descriptive approach was employed through field observation, in-depth interviews with the business owner, and documentation analysis. The analysis is grounded in the 4P marketing mix framework — Product, Price, Place, and Promotion. The findings reveal that Dua Karunia builds its competitiveness through four strategic pillars: (1) product differentiation through multi-flavor diversification and consumer-driven customization; (2) cost-based pricing that is adaptively managed through shrinkflation tactics in response to raw material price fluctuations; (3) multi-channel distribution that integrates direct outlet sales with digital platforms (Shopee and WhatsApp); and (4) long-established word-of-mouth reputation-based promotion reinforced by active e-commerce presence. This study concludes that the consistent integration of all four marketing mix elements, supported by family-based operational flexibility and the cultural heritage value embedded in the product, constitutes the primary source of sustained competitive advantage for Dua Karunia amid increasingly intense cluster competition. These findings carry practical implications for similar SMEs seeking to design adaptive, competitiveness-oriented marketing strategies.

Inconsistent terminology across cybersecurity frameworks undermines global governance and interoperability. The National Institute of Standards and Technology Cybersecurity Framework (NIST CSF 2.0) and ISO/IEC 27001:2022 share similar objectives but diverge semantically in defining risk, control, and resilience. This semantic gap causes difficulties in compliance mapping and automated policy translation. Research Objectives: This study aims to analyze the semantic similarity and divergence between NIST and ISO/IEC 27000 terminologies, identify conceptual structures influencing interoperability, and propose an AI-assisted foundation for harmonizing cybersecurity language globally. Methodology: A mixed-method semantic comparative design integrates Natural Language Processing (NLP) and ontology mapping. Using the nist_glossary.csv dataset and ISO vocabularies, terms were normalized and analyzed via cosine similarity using sentence-transformer embeddings. Ontological alignment was visualized through the Semantic Threat Graph (STG) and validated by certified experts using Cohen’s Kappa reliability tests. Results: From 672 term pairs, results show 40.9% high semantic equivalence, 38.8% partial overlap, and 20.3% semantic divergence. Strongest alignment appears in “Protect” and “Identify” domains, while divergences occur in governance and recovery-related terms. Ontology mapping revealed three conceptual clusters—Risk Governance, Technical Safeguards, and Organizational Readiness. Conclusions: Findings confirm a 79.7% total semantic alignment, indicating strong potential for harmonizing global cybersecurity standards. The study contributes an empirical model combining computational linguistics and AI-based ontology mapping to establish semantic interoperability, enabling unified cybersecurity governance and AI-driven compliance automation. Keywords: Semantic Interoperability; Ontology Mapping; Cybersecurity Frameworks; Terminology Alignment; AI Harmonization

Transaction logs in online retail provide opportunities for data-driven customer segmentation. This study segments customers at two scopes global (all countries) and United Kingdom (UK) using Recency, Frequency, and Monetary (RFM) features derived from the Online Retail transaction dataset. After cleaning cancellations and invalid records, RFM variables are computed per customer and normalized. K-Means clustering is applied separately for global and UK data, while the number of clusters is selected via the elbow criterion and validated using internal indices. The best configuration for both scopes yields five clusters, with moderate separation quality based on the silhouette score. Cluster profiling indicates distinct groups ranging from low-frequency low-spending customers to highly frequent high-spending customers. The comparison between global and UK segmentation shows similar structural patterns, yet different proportions across segments, supporting targeted retention and value-driven marketing actions.

This study presents ReflectAI, a web-based system designed to automate the creation of teaching materials tailored to students' learning styles using behavior data from a Learning Management System (LMS). Student digital activity data—such as logins, material access, forum participation, assignment submission, and quiz results—are extracted and processed using a Hierarchical Clustering algorithm to categorize students into three learning styles: visual, auditory, and kinesthetic. Based on the clustering results, the system automatically generates personalized learning modules using generative AI (ChatGPT API), aligned with each student's learning preferences. Employing a data-driven system development approach, the system was tested with data from 230 students in a mathematics course. The results show diverse learning style distributions and relevant, tailored content generation. ReflectAI is designed to reduce teachers’ administrative workload and enhance personalized and adaptive learning. This system contributes to educational transformation through deep, data-driven technology integration.

Village-Owned Enterprises (BUMDes) are established to support the improvement of the local economy. One of the recurring challenges faced by BUMDes is capital availability, which is essential for business expansion and achieving shared objectives. This study aims to analyze the effect of cognitive bias on investment decisions through reward-based investment balance as a mediating variable. The study employed a sample of 165 BUMDes distributed across the Pati Residency, using a combination of purposive sampling and cluster sampling, where the selected BUMDes represented each district and involved investors from the local community or community groups. Data were collected from BUMDes located in five districts within the Pati Residency, and the analysis was conducted using SMART PLS. The results indicate that cognitive bias has a direct and significant effect on community investment decisions as well as on reward-based investment balance. Furthermore, reward-based investment balance was found to have a direct effect on investment decisions. The findings also confirm that reward-based investment balance mediates the relationship between cognitive bias and investment decisions.

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 increasing use of Learning Management Systems (LMS) in higher education generates large amounts of student activity data that have the potential to provide deeper insights into learning processes. However, in practice, these data are still rarely analyzed systematically to understand variations in students’ learning activity patterns, limiting their practical use in supporting teaching and learning. This study aims to explore students’ learning activity patterns in an LMS using a clustering approach based on activity data.This research utilizes the publicly available Open University Learning Analytics Dataset (OULAD), focusing on a single course and a single academic term. LMS activity data were processed through data cleaning and feature extraction, followed by student clustering using the K-Means algorithm. The quality of the clustering results was evaluated using the Silhouette Score, and visual analysis was applied to support the interpretation of the results.The results indicate that students’ learning activities can be grouped into two main patterns, namely a group of students with high learning activity and a group with lower or moderate activity levels. These findings highlight the existence of heterogeneous learning behaviors among students, even within the same learning context.The identified learning activity patterns provide an initial foundation for utilizing LMS data to monitor student engagement and to support the development of more responsive, data-driven learning approaches in higher education.

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.

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.

Forgiveness can be described as a response given by someone who has experienced hurt in order to avoid revenge and expressions of anger towards the perpetrator, while choosing to show compassion, affection, love, and positive behavior. One of the factors that influence forgiveness is emotional intelligence, which includes an individual's ability to motivate themselves, show resilience to failure, control emotions, maintain satisfaction, and regulate psychological conditions. The purpose of this study was to determine whether there is a relationship between emotional intelligence and the level of forgiveness among high school students in Montong District. This research is non experimental with a correlational analytical design using a cross sectional approach. The research population included 592 high school students in Montong District. The sampling technique applied was probability sampling with cluster random sampling, resulting in a sample of 239 students. Data collection was conducted using questionnaires that measured emotional intelligence and forgiveness. The results of the analysis using the Spearman test at α = 0.05 showed p = 0.000 < 0.05 with a correlation coefficient of r = 0.389, indicating a low relationship between the two variables, thus accepting the hypothesis. Based on this description, it can be concluded that there is a relationship between emotional intelligence and the level of forgiveness among high school students in Montong District.

Graph theory as a branch of discrete mathematics has experienced significant development in its application to modern complex network systems, particularly in digital social networks and transportation systems. This research aims to analyze fundamental concepts of graph theory, examine characteristics of cycle detection algorithms along with their computational complexity, investigate their application in digital social network analysis, and explore their implementation in digital transportation system optimization. The research method employs a qualitative approach with library research focusing on scientific literature from 2020-2025 period from accredited academic databases such as Scopus, Web of Science, and IEEE Xplore, utilizing thematic analysis techniques to identify meaningful patterns from the examined literature. Research findings indicate that fundamental graph theory concepts including vertices, edges, and graph classifications form the foundation for relational structure modeling. Cycle detection algorithms such as Depth-First Search, Union-Find, and Tarjan demonstrate effectiveness with O(V+E) complexity for large-scale graphs. Applications in digital social networks facilitate community identification through Multi-View Clustering, centrality analysis for influencer detection, and understanding viral information dissemination patterns. Implementation in digital transportation systems demonstrates route planning optimization using Dijkstra and Bellman-Ford algorithms, vulnerability analysis through articulation point and bridge identification, and bottleneck detection with betweenness centrality. The research concludes that integration of graph theory in discrete mathematics education enhances critical thinking skills and real-world application understanding, with recommendations for algorithm development for massive dynamic graphs and machine learning integration in graph algorithm optimization.

The creative economy industry serves as a strategic pillar of the national economy, experiencing significant transformation in the digital era. This study aims to comprehensively analyze the pattern of human resource (HR) competency gaps within priority subsectors of Indonesia's creative economy and formulate effective, multi-stakeholder development strategies. Employing a Systematic Literature Review (SLR) methodology, this research rigorously analyzes 30 scientific journal articles, government reports, and publications from global institutions published between 2014 and 2024. The findings delineate three primary clusters of competency gaps: (1) The Digital-Technical Competency Gap, encompassing deficiencies in data analytics, specialized software mastery, and digital content creation tools; (2) The Digital-Business Competency Gap, which includes shortcomings in digital financial literacy, online business model development, and management of digital intellectual property rights; and (3) The Social-Cognitive Competency Gap, highlighting needs in adaptability, complex problem-solving, and effective virtual collaboration. In response, this paper proposes an integrative strategic framework grounded in a collaborative multi-stakeholder approach. Key recommendations include revitalizing educational curricula through industry-embedded learning and micro-credential integration, developing agile and accessible training ecosystems featuring bootcamps and digital platforms, and fostering supportive policies through fiscal incentives and the alignment of national qualification frameworks with digital skill standards. The successful implementation of this synergistic strategy is expected to significantly enhance the adaptability, innovation capacity, and global competitiveness of Indonesia's creative workforce, thereby ensuring the sustainable growth of the creative economy sector in the face of rapid digital disruption.

This study explores the role of visual analytics in enhancing decision-making processes within creative industries, focusing on its application to large-scale multimedia datasets. Visual analytics integrates interactive visualization techniques with computational algorithms, enabling users to explore complex datasets intuitively and derive actionable insights. The research centers on the design and implementation of interactive dashboards tailored to the creative sector, particularly film, music, and advertising industries, to facilitate real-time data exploration. The study also investigates the usability of these tools through expert-based evaluations, aiming to assess their effectiveness in supporting informed and timely decision-making. The findings reveal that interactive visualizations significantly improve insight discovery and pattern recognition, enabling decision-makers to uncover hidden trends in large multimedia datasets. However, challenges related to scalability, user acceptance, and real-time processing were encountered during the implementation phase. The research highlights the practical benefits of integrating visual analytics into industry workflows, which include enhanced content creation, audience engagement, and strategic planning. Furthermore, the study identifies key visual analytics techniques such as dynamic dashboards, pattern recognition, data mining, and clustering, which are essential for analyzing multimedia data. The study concludes by emphasizing the potential for wider applications of visual analytics in other sectors, suggesting future research directions to improve tool performance, scalability, and user accessibility, as well as exploring the integration of emerging technologies like artificial intelligence and virtual reality.