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The utilization of digital technology in the Industry 4.0 era has become crucial for vocational high school (SMK) students to build economic independence. However, partners at SMKN 1 Selo still face obstacles in optimizing social media and digital platforms as effective promotional tools for student work and self-development. This community service activity aims to enhance students' understanding and skills in using social media (Instagram, TikTok) and other digital platforms as creative marketing instruments. The method employed is a descriptive qualitative approach through participatory training, which includes material presentation on digital branding strategies, visual content creation practices, and copywriting techniques. The results of the activity indicate a significant improvement in students' technical abilities, where they demonstrated the capacity to produce engaging promotional content and understand social media algorithms to reach a wider audience. This study aims to describe the process and impact of utilizing social media and digital platforms as promotional tools for SMKN 1 Selo students. The primary focus of this activity is to provide a deep understanding of personal branding strategies and creative product marketing in the digital age. Through this program, it is expected that SMKN 1 Selo students will possess higher digital competitiveness and the ability to leverage the digital ecosystem to support the promotion of vocational products and their professional profiles in the future.

Educator certificates are official documents that play a crucial role for teachers, as they serve as legal proof of professional competence and are required for various administrative purposes, such as professional allowance applications, promotion, transfer, and institutional accreditation. Along with the increasing number of educators in Indonesia, the volume of educator certificate data managed by educational institutions has also grown significantly. However, certificate management is still largely conducted in a conventional manner, functioning merely as digital or physical archives without an effective search mechanism, resulting in inefficiencies and difficulties in retrieving relevant documents. Therefore, an information retrieval approach is needed to support fast and accurate document searching. This study aims to analyze and implement an information retrieval system for educator certificates using the Cosine Similarity method. The research data consist of educator certificate documents, including professional educator certificates, training certificates, and competency certificates. The retrieval process involves text preprocessing, term weighting using TF-IDF, and similarity measurement using Cosine Similarity. The results show that document d1 (Professional Mathematics Educator Certificate) has the highest similarity value to the query “educator certificate,” as it contains all query terms with relatively high TF-IDF weights. Document d3 ranks second due to partial term similarity, while document d2 has the lowest similarity value because it shares only one common term with the query. These findings indicate that the Cosine Similarity method is effective in ranking educator certificate documents based on their content relevance in an objective and measurable manner. The proposed system can improve the efficiency and accuracy of educator certificate document management and retrieval in educational institutions.

Introduction: Clean and Healthy Living Behavior (PHBS) is an important indicator in improving health status, particularly among university students as a productive age group. Health knowledge plays a role as a predisposing factor in the formation of healthy behavior. Individuals with a good level of health knowledge tend to have higher awareness and motivation to implement PHBS. Conversely, a lack of knowledge may lead to behaviors that pose health risks. Objective: This study aimed to determine the relationship between the level of health knowledge and clean and healthy living behavior (PHBS) among students at Universitas Muhammadiyah Semarang. Methods: This study employed a quantitative design with a cross-sectional approach. The sample consisted of 55 students, including students from health-related and non-health-related faculties. Samples were selected using an accidental sampling technique, namely respondents who were encountered and willing to complete the questionnaire at the time of data collection. The research instruments were a health knowledge questionnaire and a PHBS questionnaire. Data analysis was conducted using univariate and bivariate analysis with the Spearman test. Results: The results showed that the majority of respondents had good health knowledge (89.1%) and good PHBS (90.9%). The Spearman test results showed a p-value of 0.001 (p < 0.05), indicating a significant relationship between the level of health knowledge and PHBS. Conclusion: It can be concluded that the better the level of students’ health knowledge, the better the implementation of clean and healthy living behavior.

This research examines the coordination efforts of the Karanganyar District Disaster Management Agency (BPBD) in managing disasters, identifying obstacles, and exploring efforts to strengthen these coordination efforts. Utilizing a qualitative approach with a case study method, the study aims to investigate the mechanisms of cross-sector coordination, communication patterns, and division of labor within BPBD. Data was gathered through in-depth interviews, observations, and document studies, and analyzed using an interactive model. The findings indicate that BPBD's coordination is relatively effective, demonstrated through unified action, a multi-channel communication system, and a clear division of labor. However, challenges remain, including the complexity of cross-sectoral coordination, differences in capacity and authority, and the pressure of emergency situations. The study concludes that improving coordination requires enhancing staff discipline, developing human resource capacity, and strengthening an integrated work system. These efforts are vital for improving the overall effectiveness of disaster management at the local level, ensuring a more efficient and comprehensive response to disasters in the future.

This study aims to explore and deepen the understanding of the doctrine of the Holy Trinity through the approach of Hindu–Christian meditative dialogue. The doctrine of the Holy Trinity constitutes the central foundation of the Christian faith, understood as the unity and equality of the essence of the one God in three persons, namely the Father, the Son, and the Holy Spirit. However, explanations and interpretations of the Trinitarian doctrine are often regarded as complex and difficult to comprehend, not only by the general public but also by many Christians, particularly lay believers. Therefore, an alternative approach that is more communicative and contextual is needed so that this doctrine can be understood in a simpler and more reflective manner. One approach proposed in this study is the concept of Hindu–Christian meditative dialogue, which emphasizes the contemplative dimension and inner spiritual experience as means of theological understanding. This study employs a qualitative method using a literature-based approach, drawing on theological documents, books, scholarly journals, and other relevant literature as the primary data sources. The collected data and information are then systematically analyzed and elaborated to formulate a more accessible conceptual understanding of the doctrine of the Holy Trinity through Hindu–Christian meditative dialogue. The findings indicate that Hindu–Christian meditative dialogue can serve as an effective medium for explaining and understanding the doctrine of the Holy Trinity in a clearer, deeper, and more contextual manner.

This study investigates the integration of computational thinking (CT) principles with adaptive curricula to enhance problem-solving skills in undergraduate programming education. Traditional programming curricula often emphasize syntax and basic concepts, neglecting critical problem-solving strategies. The adaptive curriculum framework used in this study combines CT skills such as decomposition, pattern recognition, abstraction, and algorithmic thinking with personalized learning experiences. A mixed-method approach, combining qualitative and quantitative research, was employed to assess the effectiveness of this integrated approach. The results show significant improvements in students' problem-solving abilities, conceptual understanding, and engagement compared to a control group following a traditional curriculum. Students in the experimental group, which received the adaptive curriculum, demonstrated better performance in applying algorithms and debugging code. Additionally, students expressed higher levels of engagement and motivation, suggesting that the personalized learning environment fostered greater academic involvement. The study highlights the importance of integrating CT principles with adaptive learning frameworks to create a more inclusive and effective learning environment that accommodates diverse learning needs. The findings suggest that adaptive curricula can bridge gaps in traditional education by providing personalized support and ensuring that students progress at their own pace. This approach is especially beneficial for programming education, where both conceptual understanding and practical problem-solving skills are critical for success. Future research should explore the long-term impact of adaptive learning frameworks and investigate how these technologies can be integrated with traditional teaching methods to maximize their effectiveness.

In high-performance computing (HPC) environments, the need to balance memory efficiency and query performance is crucial for ensuring optimal system performance. Traditional data structures, such as B-trees and hash tables, often prioritize either memory usage or query speed, leading to suboptimal performance in memory-constrained systems. This paper proposes a hybrid data structure that combines the strengths of multiple traditional data structures to optimize both memory usage and query processing speed. The proposed hybrid structure integrates cache-conscious algorithms, dynamic memory allocation, and compression techniques for intermediate query results. The approach is evaluated through extensive benchmarking tests comparing it to standard data structures like B-trees and hash tables under various workloads. Results show that the hybrid data structure reduces memory overhead by up to 30% while maintaining query processing speeds up to 1.5 times faster than conventional methods. Furthermore, the hybrid structure demonstrates robust performance across different types of queries, including both point and range queries, ensuring versatility and efficiency. The findings indicate that this hybrid approach provides a promising solution for HPC systems, where both memory efficiency and query speed are essential. Future research can explore extending the hybrid structure to distributed systems and emerging technologies, further improving its scalability and adaptability to new computational paradigms.

This study investigates the role of Information Technology (IT) governance in enhancing risk management performance and ensuring regulatory compliance within multinational digital enterprises. As digital transformation continues to reshape the global business landscape, organizations face increasing challenges in managing technological risks and complying with complex regulatory requirements across various jurisdictions. The study adopts a quantitative approach, using a survey methodology to collect data from senior IT and compliance managers in multinational digital enterprises. The survey focuses on how IT governance frameworks, such as COBIT 2019 and ISO 27000, are utilized to align IT strategies with business objectives, mitigate risks, and maintain regulatory compliance. The findings indicate that organizations with well-established IT governance structures are better positioned to proactively identify and mitigate risks, ensuring greater consistency in meeting regulatory requirements. These organizations demonstrate improved risk management effectiveness, especially concerning cybersecurity, data privacy, and compliance with global regulations like GDPR. In contrast, organizations with ad hoc or decentralized governance structures struggle with fragmented risk management and compliance efforts. The study further highlights the importance of integrating IT governance frameworks with internal audit functions, specifically the Chief Audit Executive (CAE), to enhance cybersecurity resilience and ensure compliance with global standards. This research contributes to the literature by providing empirical evidence on the integration of IT governance, risk management, and regulatory compliance in multinational enterprises. It also highlights the need for a structured and systematic approach to IT governance to improve organizational performance in managing risks and ensuring consistent regulatory adherence. The study offers practical insights for organizations looking to optimize their IT governance structures in the face of rapid digital transformation.

The rapid proliferation of Internet of Things (IoT) devices across diverse industries has significantly increased the vulnerability of IoT edge networks to sophisticated cyber threats. Traditional intrusion detection systems (IDS), such as signature-based and anomaly-based approaches, are often insufficient in addressing the dynamic and evolving nature of these threats. This study proposes a hybrid intrusion detection system (IDS) framework that combines supervised machine learning (ML) techniques with deep reinforcement learning (DRL) to enhance detection performance in real-time, resource-constrained IoT environments. The proposed framework utilizes supervised learning for initial traffic classification and DRL for adaptive decision-making, enabling the system to continuously learn and optimize its detection policies based on new attack patterns. The hybrid approach significantly improves detection accuracy and reduces false positives when compared to conventional signature-based and single-model ML systems. In addition to improved detection capabilities, the framework's computational efficiency allows it to operate effectively within the constraints of IoT devices, ensuring that it is suitable for large-scale deployments. Benchmark evaluations using publicly available datasets, such as NSL-KDD, IoT-23, and BoT-IoT, show that the hybrid IDS framework outperforms traditional methods, providing a more robust and adaptive solution to cybersecurity challenges in IoT edge networks. The findings of this study suggest that combining machine learning with deep reinforcement learning offers a promising approach to secure IoT environments and address the limitations of existing IDS techniques. Future work will explore enhancing real-time adaptability, scalability, and the detection of zero-day attacks in evolving IoT ecosystems.

The banking sector plays a strategic role in supporting financial system stability and capital market development. Market performance, reflected through stock returns, represents investor confidence in a firm’s prospects and sustainability. In recent years, investors have increasingly considered non-financial factors such as intellectual capital and corporate social responsibility in evaluating firm value. However, empirical findings regarding the effect of these factors on market performance remain inconsistent, particularly in the Indonesian banking sector. This study aims to examine the effect of intellectual capital and corporate social responsibility on market performance of conventional commercial banks listed on the Indonesia Stock Exchange during the 2021–2024 period. This research employs a quantitative approach using secondary data obtained from annual reports and sustainability reports. Intellectual capital is measured using the Value Added Intellectual Coefficient method, while corporate social responsibility is measured using a disclosure index based on the Global Reporting Initiative. Market performance is proxied by stock returns. Data analysis is conducted using multiple linear regression with the Ordinary Least Squares approach. The results indicate that intellectual capital and corporate social responsibility have a positive and significant effect on market performance. These findings suggest that effective management of intangible assets and social responsibility disclosure can enhance investor perception and firm value. The results provide important implications for bank management in formulating value-enhancing strategies and for investors in making investment decisions.  

The Halal Management Sistem (HMS) has often been positioned as an administrative compliance tool that focuses on regulatory and certification requirements. This approach has the potential to simplify the meaning of halal by overlooking the inherent values, especially in the context of Muslim businesses. This study aims to interpret the understanding of the Halal Management Sistem not only as an administrative compliance mechanism, but also as an expression of worship and spiritual commitment in business practices. This study uses a qualitative approach through a literature review of scientific articles, books, and relevant publications discussing halal management, Islamic business ethics, and the perspectives of business actors. The results of the study show that the HMS is understood by business actors as a manifestation of religious obedience integrated into economic activities, where the application of halal principles is seen as part of devotion to God and a form of moral responsibility in running a business. In this understanding, administrative compliance is positioned as an implication of spiritual awareness, not as the main objective of the sistem's implementation. The research findings also indicate that the spiritual meaning of SMH encourages consistency in halal practices and strengthens long-term business orientation. Conceptually, this study has implications for the development of halal management studies by placing the spiritual dimension as the main foundation in the implementation of the Halal Management Sistem, particularly in supporting sustainable and ethical business practices.

Reading ability is a basic skill that plays a crucial role in the success of students' learning process. However, various studies indicate that the reading ability of junior high school students remains low. This study aims to analyze the reading ability of students at the UPT SMP Negeri 2 Mappakasunggu using Alfred Schutz's social phenomenology perspective. The research approach used was qualitative with descriptive methods. Data collection techniques included in-depth interviews, observation, and documentation of students and teachers. The results indicate that students' low reading ability is not solely caused by cognitive factors but is also influenced by subjective meanings formed through students' social experiences. The lack of a literacy culture in the family and school environment results in reading not being perceived as an important or enjoyable activity. Furthermore, the dominant use of gadgets for entertainment creates habits that reduce students' interest and concentration in reading texts. From Alfred Schutz's social phenomenology perspective, these conditions are related to students' lifeworlds and stock of knowledge, which shape their perspectives and actions toward reading. This study concludes that improving students' reading ability requires a comprehensive approach, taking into account experiences, social interactions, and the formation of meaning in reading in students' daily lives.

This research examines the disproportionate allocation of legal responsibility between doctors and nurses in Indonesia’s health care system and proposes measures to reorganize accountability in a more fair and proportional way. The issue addressed stems from shortcomings in existing regulations, which have not yet ensured legal certainty or balanced legal protection for both professions in clinical practice. The study uses a normative juridical method, applying both statutory and conceptual approaches. The statutory approach reviews laws and regulations that govern professional authority and legal liability of doctors and nurses, while the conceptual approach analyzes legal principles, doctrines, and concepts related to professional responsibility in health services. Legal materials are collected through library research, including primary, secondary, and tertiary legal sources, and are analyzed using qualitative descriptive methods. The results show that although statutory provisions formally regulate the distribution of authority and responsibility between doctors and nurses, there are still normative uncertainties, overlapping regulations, and legal gaps. These issues may lead to an unequal burden of legal responsibility, particularly in cases involving medical errors or negligence. Such conditions weaken legal protection for nurses and can negatively affect the quality of health care delivery. The study concludes that regulatory reform is needed to clarify legal responsibility in accordance with professional authority and to implement a more just and proportional system of accountability. The findings are expected to enrich health law studies and provide guidance for policymakers in developing a fairer legal responsibility framework for health professionals.  

The objective of this study is to comprehensively examine and analyze the influence of microstructure on the mechanical properties of metallic materials. Microstructure is known to play a crucial role in determining the mechanical behavior of metals; therefore, a thorough understanding of this relationship is essential for the development of engineering materials. This study adopts a systematic literature review approach, employing descriptive analysis and meta-analysis of recent scientific publications obtained from various reputable academic databases. The analysis results indicate that microstructure is a significant determinant of the mechanical characteristics of metallic materials, including strength, ductility, and resistance to deformation. The most influential microstructural parameters include grain size, phase distribution, crystallographic orientation, dislocation density, and the presence and characteristics of precipitates. Among these parameters, grain size has been shown to be the most dominant factor. The Hall–Petch strengthening mechanism demonstrates that grain refinement can increase the tensile strength of materials by approximately 200 to 300 percent. In addition, materials with multi-phase microstructures, such as dual-phase steels and TRIP steels, exhibit an excellent combination of strength and ductility compared to single-phase materials. Based on the meta-analysis results, each metallic material system exhibits a trade-off between strength and ductility, whereby an increase in strength is generally accompanied by a reduction in ductility. These findings indicate that achieving an optimal combination of mechanical properties for specific application requirements necessitates a comprehensive and integrated microstructural engineering approach.

Malaria remains a major public health problem in Papua, Indonesia, with a high incidence rate and significant hematological complications. One of the main impacts of malaria infection is anemia caused by a decrease in hemoglobin levels. This study aimed to analyze the relationship between hemoglobin levels and malaria parasite species among patients at Sentani Health Center, Jayapura Regency. This was an analytical study with a cross-sectional approach. A total of 50 malaria-positive patients confirmed by microscopic examination were included as samples. Data collected included gender, parasite species, nutritional status, hemoglobin level, and parasite count, analyzed using the Chi-Square test with a 95% confidence level (α=0.05). The results showed that most respondents were male (54%) and predominantly infected by Plasmodium falciparum (66%). The majority had normal nutritional status (40%), and the distribution between normal and abnormal hemoglobin levels was equal (50% each). Statistical analysis revealed a significant association between body mass index and hemoglobin level (p=0.03), but no significant relationship between parasite species and hemoglobin level (p=0.145). These findings indicate that nutritional status plays a more dominant role in determining hemoglobin levels than parasite species variation. In conclusion, anemia among malaria patients in endemic regions is influenced not only by the infecting Plasmodium species but also by individual nutritional factors. Integrated interventions focusing on nutritional improvement and malaria control are essential to reduce anemia risk in endemic areas such as Papua. Malaria remains a major public health problem in Papua, Indonesia, with a high incidence rate and significant hematological complications. One of the main impacts of malaria infection is anemia caused by a decrease in hemoglobin levels. This study aimed to analyze the relationship between hemoglobin levels and malaria parasite species among patients at Sentani Health Center, Jayapura Regency. This was an analytical study with a cross-sectional approach. A total of 50 malaria-positive patients confirmed by microscopic examination were included as samples. Data collected included gender, parasite species, nutritional status, hemoglobin level, and parasite count, analyzed using the Chi-Square test with a 95% confidence level (α=0.05). The results showed that most respondents were male (54%) and predominantly infected by Plasmodium falciparum (66%). The majority had normal nutritional status (40%), and the distribution between normal and abnormal hemoglobin levels was equal (50% each). Statistical analysis revealed a significant association between body mass index and hemoglobin level (p=0.03), but no significant relationship between parasite species and hemoglobin level (p=0.145). These findings indicate that nutritional status plays a more dominant role in determining hemoglobin levels than parasite species variation. In conclusion, anemia among malaria patients in endemic regions is influenced not only by the infecting Plasmodium species but also by individual nutritional factors. Integrated interventions focusing on nutritional improvement and malaria control are essential to reduce anemia risk in endemic areas such as Papua.

This research aims to develop a multiclass flower image classification system using the Convolutional Neural Network (CNN) algorithm with the EfficientNet architecture. The main problem addressed is the difficulty of manual identification of flower species that share high visual similarity. The research stages include collecting 17,299 flower images across 19 classes, performing data preprocessing such as image resizing, pixel normalization, and augmentation, followed by model training using the EfficientNet transfer learning approach. The model was trained for 10 epochs with an 80:20 training-validation data split. The evaluation results show that the model achieved a validation accuracy of 98.05% with a loss value of 0.0968, and an average precision, recall, and F1-score of 0.98. The trained model was then implemented into a web-based application built using the Next.js framework, enabling users to upload flower images and obtain real-time classification results via the Hugging Face API. The system successfully identified flower species with a confidence level of 99.87%. These findings demonstrate that combining a modern CNN architecture with transfer learning provides efficient and highly accurate flower classification performance, which can be effectively implemented for educational and digital conservation purposes.

This study investigates the role of Enterprise Information Systems (EIS) in driving innovation within organizations. The research employs a mixed-method approach, combining survey-based structural analysis and in-depth organizational case studies to explore how different EIS capabilities influence organizational innovation. The study focuses on four key EIS capabilities: functional capabilities such as workforce management and customer value creation; technological capabilities including ERP systems and real-time analytics; dynamic capabilities, especially organizational learning; and collaborative innovation through external partnerships. The survey results reveal that EIS capabilities, particularly data analytics and integration, significantly enhance organizational agility, decision-making, and innovation outcomes. In-depth case studies provide detailed insights into how these capabilities are applied in real-world organizational settings, illustrating their impact on process and service innovation. The findings indicate that the effective integration of EIS across organizational functions, along with improved access to data, contributes to operational efficiency and innovation success. However, challenges such as integration issues, resistance to change, and lack of skilled personnel were also identified as barriers to successful EIS adoption. The study contributes to the literature by offering a comprehensive understanding of how EIS capabilities drive innovation and highlighting the importance of organizational culture and leadership in the adoption process. The research provides practical recommendations for organizations to leverage EIS for fostering innovation, such as focusing on EIS integration, overcoming organizational barriers, and ensuring leadership engagement. Finally, the study suggests future research directions, including the refinement of multi-method approaches and the need for longitudinal studies to better understand the long-term impact of EIS on innovation outcomes.

This research explores the integration of Enterprise Architecture (EA) and Artificial Intelligence (AI) to optimize strategic decision-making in digital service-oriented organizations. These organizations often face challenges such as fragmented decision-making due to disconnected IT systems and limited data-driven insights. The objective of the study is to develop an integrated framework that combines EA and AI to enhance decision-making accuracy, operational efficiency, and strategic alignment. The study employs design science research methodology, involving the development of the framework, expert validation, and testing in simulated organizational scenarios. The findings reveal that the integrated framework improves decision-making by providing real-time, data-driven insights, predictive analytics, and better alignment with organizational goals. AI's role in analyzing large datasets and generating actionable insights allows decision-makers to anticipate future trends and make more informed decisions. The framework significantly outperforms traditional EA approaches, particularly in terms of predictive decision support and adaptive intelligence. The study concludes that the integration of EA and AI provides a robust solution for organizations looking to improve strategic decision-making, enhance operational efficiency, and stay competitive in dynamic business environments.

The integration of Customer Relationship Management (CRM) and Enterprise Resource Planning (ERP) systems has emerged as a critical strategy for modern digital enterprises aiming to enhance customer experience and operational efficiency. This study examines the impact of CRM-ERP integration on customer satisfaction, personalized service, and organizational responsiveness. By adopting a mixed-methods approach, this research combines quantitative customer data analysis and qualitative managerial interviews to assess the benefits and challenges of CRM-ERP integration. Key findings highlight significant improvements in customer experience, with increased satisfaction and personalized interactions facilitated by a unified view of customer data. Operational efficiencies were also realized through streamlined processes, better alignment of departments, and enhanced decision-making based on real-time, data-driven insights. Despite these positive outcomes, challenges such as system integration complexities, data fragmentation, and resistance to change were identified, which hindered the speed of integration and full utilization of the systems. This study demonstrates that CRM-ERP integration provides a competitive advantage by improving both customer service and business agility, particularly in industries undergoing digital transformation. For digital enterprises, integrating these systems is crucial for maintaining a seamless customer experience across various touchpoints and achieving greater operational effectiveness. The paper concludes by suggesting future research on the long-term impact of CRM-ERP integration on customer loyalty, business growth, and the potential role of emerging technologies like AI and blockchain in further enhancing these systems.

This research focuses on the design and evaluation of a novel parallel graph optimization algorithm incorporating dynamic load balancing (DLB) to address inefficiencies in heterogeneous computing environments. Large-scale graph optimization problems, such as those in social networks, bioinformatics, and transportation systems, often suffer from computational imbalances when using traditional static load balancing approaches, leading to underutilized resources and prolonged execution times. The primary objective of this research is to develop an algorithm that can dynamically adjust workload distribution across processors, enhancing computational efficiency and scalability. The proposed method combines heuristic techniques, including region expansion and multilevel partitioning, with diffusive load balancing strategies to minimize inter-processor communication overhead. Experimental results demonstrate that the proposed algorithm reduces execution time by up to 40% compared to static methods, with optimized resource utilization and more balanced workload distribution. The scalability of the algorithm is also evident, as it adapts effectively to increasing problem sizes and processor counts. These findings suggest that dynamic load balancing is crucial for improving parallel graph optimization in real-world applications. Future work will focus on further enhancing the algorithm’s responsiveness to rapidly changing workloads and expanding its applicability to additional domains.