Publication Search

The development of digital technology in the Society 5.0 era presents both challenges and opportunities in history teaching, particularly regarding teachers’ ability to integrate technology and adapt to changes in students’ learning styles. This study aims to analyse the challenges faced by teachers and the strategies employed in digital-based history teaching. The method used is a Systematic Literature Review (SLR) employing the PRISMA approach, involving the stages of identification, screening, eligibility assessment, and inclusion of scientific articles published between 2020 and 2025. From the selection process, 25 relevant articles were identified for qualitative descriptive analysis. The findings indicate that teachers’ digital literacy levels remain at a moderate level and are unevenly distributed, thereby affecting the sub-optimal integration of technology into teaching. Furthermore, changes in students’ learning styles which are increasingly technology-oriented require more interactive and contextual pedagogical innovations. On the other hand, the use of digital media has proven capable of enhancing student motivation and learning outcomes, although this is still hindered by limitations in infrastructure and teachers’ technical competencies. The abundance of historical information on the internet also poses challenges regarding the validity of sources, thus necessitating the strengthening of digital literacy and critical thinking. The implications of this research underscore the importance of the ongoing development of teachers’ digital skills, the improvement of educational facilities, and the implementation of innovative teaching models to ensure that history teaching becomes more relevant and effective, and is capable of fostering critical thinking skills in pupils in the digital age.

A mid the growing complexity of contemporary education, the challenge of integrating intellectual achievement with moral and spiritual development has become increasingly urgent. This study examines educational strategies in the Qur’an through the perspective of tafsir tarbawi, focusing on four core approaches: exemplary conduct, advice, dialogue, and storytelling. The research aims to explore how the Qur’an presents pedagogical methods that not only convey knowledge but also shape character and internalize values in learners. Employing a qualitative library research design with thematic and tafsir tarbawi approaches, this study analyzes relevant Qur’anic verses and supporting scholarly literature. The findings reveal that the Qur’an offers a holistic and dynamic model of education in which exemplary conduct functions as a behavioral model, advice serves as a means of value internalization, dialogue promotes reflective and critical thinking, and storytelling strengthens moral awareness through narrative experience. These strategies are interconnected and operate across cognitive, affective, and behavioral domains. The study concludes that Qur’anic educational strategies remain highly relevant for contemporary Islamic education, particularly in strengthening character formation, reflective learning, and the integration of values in educational practice.

This study aims to analyze the influence of entrepreneurship education and motivation on students' entrepreneurial interest. The background of this study is based on the low interest of students in entrepreneurship despite having received entrepreneurship education at university. This study uses a quantitative approach with an associative method. The study population was students of the Management Study Program, Class of 2022, STIE Bina Karya Tebing Tinggi, with a purposive sampling technique in accordance with certain criteria determined by the researcher. Data were collected through questionnaires and analyzed using multiple linear regression. The results show that motivation has a positive and significant effect on entrepreneurial interest, while entrepreneurship education has no significant effect partially. However, simultaneously, both variables have a significant effect on entrepreneurial interest. The coefficient of determination shows that the variables of entrepreneurship education and motivation can explain 54.8% of entrepreneurial interest, while the remainder is influenced by other factors outside the study. The implications of this study emphasize the importance of increasing motivation and strengthening more practical entrepreneurship learning to be able to encourage student interest in entrepreneurship.

Education is a fundamental element in an individual's life that aims to optimize human potential across cognitive, affective, and psychomotor domains. This research is motivated by the importance of students' ability to manage their study time effectively to support optimal learning outcomes. The purpose of this study is to describe and compare students' study time management based on gender. This study used a descriptive comparative method, with a sample of several eighth-grade students at Islamic Boarding School X. Data were collected using a Likert scale instrument, then analyzed using descriptive techniques and an independent-samples t-test using the (Jeffreys’s Amazing Statistics Program) JASP application. The results indicate that several students are able to manage their study time well. Furthermore, no significant differences were found between male and female students in managing their study time. These findings indicate that the ability to manage study time is relatively even and is not influenced by gender.

Group-based learning in the classroom plays a vital role in enhancing social interaction, individual responsibility, as well as students' critical thinking and collaborative skills. However, its implementation often faces challenges such as the dominance of certain members, social loafing, low participation, and interpersonal conflicts that hinder group effectiveness. This study aims to comprehensively examine the dynamics of learning groups by integrating four key aspects: the concept of group dynamics based on the Tuckman model, the characteristics of effective groups in cooperative learning, group formation techniques, and conflict management strategies. The research utilizes a qualitative approach with a literature study method, analyzing 25 sources including nationally accredited journals, academic books, and theses published between 2020 and 2024. Data analysis was conducted through reduction, thematic classification, content analysis, and conceptual synthesis. The results indicate that effective group dynamics can be achieved through the Tuckman stages, the application of the five elements of cooperative learning, the selection of appropriate group formation techniques with risk mitigation, and the implementation of the Thomas-Kilmann conflict management styles.The scientific contribution of this research is the development of an integrative model based on these four aspects, which serves as a conceptual framework to strengthen collaborative learning practices in the classroom. Practical implications include the formation of ideal groups consisting of 4–5 students, the establishment of initial group contracts, the use of dual assessment rubrics (individual and group), and peer evaluation mechanisms to enhance accountability and reflection.

This study examines customer churn prediction in subscription-based telecommunications from a digital marketing perspective using machine learning. The analysis utilizes a secondary dataset of 7,043 customer records that simulate behavioral, contractual, and financial attributes commonly found in telecom services. Three classification algorithms Logistic Regression, Random Forest, and Gradient Boosting are applied to model churn behavior. Data preprocessing includes handling missing values, encoding categorical variables, and splitting data into training and testing sets. Model performance is evaluated using accuracy, recall, and ROC-AUC, with emphasis on recall due to its importance in identifying at-risk customers. The results show that Gradient Boosting achieves the highest overall performance with an ROC-AUC of 0.84, while Logistic Regression provides relatively higher recall. Key drivers of churn include short-term contracts, higher monthly charges, and lower service engagement. However, recall remains moderate, indicating limitations in capturing complex behavioral factors. These findings suggest the need to combine predictive models with behavioral insights and highlight the importance of early customer engagement and long-term retention strategies.

The development of digital technology has driven significant changes in modern learning methods by integrating various multimedia media. However, multimedia's ability to facilitate learning depends largely on the extent to which its presentation aligns with the principles of learning psychology, particularly those outlined in the Cognitive Theory of Multimedia Learning (CTML). This study aims to examine the role and effectiveness of multimedia in transforming modern learning methods by combining the results of recent empirical research from 2020 to 2024. This study used the Systematic Literature Review (SLR) method by analyzing 22 journal articles obtained from several databases such as Scopus, Web of Science, ScienceDirect, Google Scholar, and Sinta. Research shows that the use of interactive multimedia helps increase student enthusiasm for learning, participation, understanding of concepts, and the ability to remember course material. However, disorganized multimedia design can add unnecessary mental burden, thereby reducing learning effectiveness. These findings suggest that the successful use of multimedia depends not only on the level of technological advancement but also on the quality of learning design that applies cognitive theory. This study provides a comprehensive summary of the latest research and provides tangible benefits for educators in creating effective digital learning content.

The results of the 2022 PISA indicate that science skills and interest in Indonesia are still low. One way to increase interest in science is by introducing inventor figure of science and techonology. However, alternative learning media that are communicative and relevant to adolescents' media consumption habits are needed. Therefore, this study aims to design a character design for an inventor in the field of science and technology for a Webtoon comic as an educational tool for adolescents aged 15–18. This study used qualitative and quantitative methods. The qualitative method was used to understand the needs and interests of the target audience and to gather information from the literature for scientific sources in designing the inventor character. The quantitative method was used as one of the character design evaluation criteria, based on voting results by adolescents aged 15–18. The character design used Marika Nieminen's principles: demographics, silhouette and shape, color, and characteristics. Several alternatives were used to generate character designs, which were then evaluated to determine the final design. The character design evaluation will be assessed using several variables within an evaluation framework.

Bullying is a psychosocial hazard that can occur in the school environment and may negatively affect students’ mental health, social interactions, and learning processes. Bullying can take various forms, including physical, verbal, and psychological actions that occur repeatedly and cause victims to experience emotional distress, decreased learning motivation, and feelings of insecurity at school. This Community Service Program (PKM) aimed to increase high school students’ knowledge and awareness regarding the dangers of bullying and its prevention through the principles of Occupational Safety and Health (OSH). The methods used in this activity included delivering educational materials through lectures, conducting question-and-answer sessions to assess students’ initial understanding, and facilitating interactive discussions to encourage active participation. Monitoring and evaluation were also conducted to assess the effectiveness of the activity. The results of the program indicated a significant improvement in students’ understanding and awareness of the forms, impacts, and prevention strategies related to bullying in the school environment. Students also demonstrated a more critical attitude toward bullying behavior and a greater awareness of the importance of creating a safe, healthy, and respectful school environment. The application of an OSH-based approach in bullying prevention is expected to strengthen the safety culture in schools and support the development of a conducive learning environment for all students.

Explainable artificial intelligence (XAI) has become a critical requirement in cybersecurity due to the high-stakes nature of security decision-making and the limitations of black-box learning models. This study investigates the construction of an explainable cybersecurity knowledge representation by leveraging standardized terminology from the NIST cybersecurity glossary. The primary problem addressed is the lack of transparent and semantically grounded reasoning mechanisms in existing AI-driven cybersecurity systems, which limits trust, accountability, and analyst adoption. To address this challenge, we propose a NIST-based semantic knowledge graph that embeds explainability directly into its ontology structure and reasoning process. The proposed framework systematically extracts definitional entities and relations from NIST glossary entries to construct a domain ontology and a multi-relational knowledge graph. A rule-based semantic relation extraction method is employed to ensure faithful, interpretable, and reproducible reasoning paths. The resulting knowledge graph contains over 3,000 cybersecurity concepts and approximately 27,000 semantic relations, covering hierarchical, associative, dependency, and mitigation semantics. Experimental evaluation demonstrates that the proposed approach achieves a high level of explainability, with 92.4% of reasoning outcomes being fully traceable and only 1.4% classified as non-traceable. Most explainable reasoning paths are limited to two or three hops, indicating an effective balance between inferential depth and human interpretability. Structural analysis further confirms the presence of meaningful hub concepts that support multi-hop semantic inference. These results confirm that ontology-driven, standard-based knowledge graphs provide a robust foundation for explainable cybersecurity intelligence. The study concludes that explainability-by-design, grounded in authoritative standards, offers a viable and trustworthy alternative to opaque AI models for cybersecurity applications.

High-dimensional clinical data exhibit complex and non-linear relationships among patient attributes, where outcomes are often influenced by feature interactions rather than isolated variables. However, many existing machine learning models prioritize predictive performance while providing limited interpretability and insufficient insight into interaction structures. This study aims to address this limitation by developing an interpretable and robust framework for feature interaction mining in clinical data. We propose a hybrid tree–neural modeling framework that explicitly captures and ranks feature interactions while maintaining stable predictive performance. Tree-based ensemble models are employed to identify non-linear interaction patterns, while neural representations enhance learning flexibility and generalization. The framework integrates interaction importance analysis, cross-validation–based stability assessment, and evaluation across multiple data splits to ensure robustness and interpretability. Experiments conducted on a real-world high-dimensional clinical dataset demonstrate that the proposed approach achieves consistent predictive performance, with AUC values ranging from 0.628 to 0.641 across five cross-validation folds (mean AUC ≈ 0.633). Performance remains stable under varying train–test splits, indicating strong generalizability. Interaction analysis reveals that a small number of dominant feature interactions—such as age combined with length of hospital stay and medication count combined with diagnostic information—consistently contribute to model predictions, appearing in over 80% of validation folds. Ablation studies further confirm that removing interaction-aware components leads to noticeable performance degradation, highlighting their importance.  In conclusion, this study demonstrates that explicit feature interaction modeling enhances interpretability, stability, and generalization in clinical prediction tasks. The proposed hybrid framework provides a reliable foundation for developing trustworthy and transparent clinical decision-support systems

This research benchmarks multiple machine learning (ML) algorithms for large-scale loan default prediction using a real-world dataset of 255,000 borrower records, where default cases represent only ~9–12% of total observations. The study addresses the persistent gap in comparative analyses of ML models that balance predictive accuracy, interpretability, and computational efficiency for credit risk assessment. Six algorithmic families were evaluated Logistic Regression, Random Forest, XGBoost, LightGBM, CatBoost, Artificial Neural Networks (ANN), and Stacked Ensemble—using standardized preprocessing, hybrid imbalance handling (SMOTE, class weighting, under-sampling), and comprehensive evaluation metrics (AUC, F1, Recall, Precision, PR-AUC, and Brier Score). Empirical results show Logistic Regression achieved the highest AUC of 0.732, outperforming nonlinear models under the baseline configuration, while LightGBM attained perfect recall (1.0) but low precision (0.116), indicating over-prediction of defaults. Gradient boosting models demonstrated robust calibration (Brier ≈ 0.114–0.116) and the best computational efficiency, with LightGBM showing the fastest training and lowest memory use. CatBoost exhibited strong recall but the slowest computation, and ANN underperformed on tabular data (AUC ≈ 0.56). The Stacked Ensemble delivered balanced results with AUC = 0.664 and improved overall stability. These findings confirm that boosting-based models, particularly LightGBM and CatBoost, offer superior scalability and calibration, whereas Logistic Regression remains a valuable interpretable baseline. The study concludes that effective default prediction requires integrating rebalancing, calibration, and threshold optimization to enhance recall and operational deployment reliability in large-scale credit ecosystems.

The Fruit of the Spirit is an important theological value to teach children because it contains points for everyday behavior. However, learning about the Fruit of the Spirit often encounters obstacles and is difficult for children aged 8-12 years to understand because it is considered too abstract and difficult to convey in simple language. Furthermore, obstacles come from the learning media used which are still conventional. Therefore, effective learning media are needed that can attract the interest and motivation of children to want to learn and apply the Fruit of the Spirit in everyday life, such as board games. In board games, there are components that can attract emotions and can be used as media, namely character pawns. The method used is the Design Thinking method with descriptive analysis. This approach is used to analyze data from the results of observations, interviews, literature studies, and questionnaires, as well as to develop concepts and design results. Thus, the character pawns can be created with a human-centered design approach, which is able to create visuals that have a close relationship with children's psychology.

The rapid development of technology in the era of the Industrial Revolution 4.0 has driven the education sector to continuously adapt to the evolving demands of digital-based industries. One of the key technological innovations supporting this transformation is the Internet of Things (IoT), which enables data acquisition, real-time monitoring, and remote control of systems through internet networks. In response to these developments, a community service program was conducted to enhance the understanding and technical skills of students at SMK Negeri 1 Sindang through the provision and utilization of an IoT Trainer Kit Simulator as a practical learning medium. This activity aimed to bridge the gap between theoretical knowledge and industry-relevant technological applications by introducing students to hands-on IoT system implementation. The program included demonstrations and guided practice on the use of sensors, microcontrollers, and web-based monitoring platforms to simulate real-world industrial scenarios. The results indicate that students showed high enthusiasm and active participation throughout the activity. Moreover, participants were able to grasp the fundamental concepts of IoT systems, understand component integration, and recognize the relevance of IoT applications in supporting automation and digital transformation. Overall, this community service activity contributed positively to strengthening students’ digital competencies and preparedness for the demands of the contemporary industrial and technological landscape.

The phenomenon of moral degradation and the increase in negative activities among children during the crucial hours between dusk and evening have become serious challenges for the community in Ujung Negeri Kahan Village. This community service research aims to evaluate and optimize the role of the "Maghrib Mengaji" program as a preventive instrument to shield children from unhealthy environmental influences and gadget dependency. The method employed in this service is participatory mentoring using the Asset-Based Community Development (ABCD) approach, involving religious leaders, parents, and village officials. Through the restructuring of activities in mosques and prayer rooms, this program focuses not only on Qur'anic literacy but also on the cultivation of noble character (akhlakul karimah). The results indicate a significant transformation in behavior; children who previously engaged in counterproductive outdoor activities are now more focused on positive spiritual and social endeavors. The success of the program is indicated by a decrease in the intensity of nighttime wandering and an increase in learning discipline. The conclusion of this activity confirms that community synergy through structured religious programs is effective in reducing the potential for juvenile delinquency and strengthening the moral resilience of the younger generation at the rural level.

Financial literacy is a fundamental competency that should be developed from an early age to foster prudent financial behavior in the future. However, the level of financial literacy among elementary school students remains relatively low, particularly in rural areas. The Community Service Program (Kuliah Kerja Nyata/KKN) serves as a strategic platform for providing financial education to the community, including elementary school students. This study aims to describe the implementation of a basic financial literacy program for students of SD 050430 Bunuraya Village through the KKN program and to analyze its impact on students’ understanding of financial concepts. This study employs a qualitative descriptive method, with data collected through observation, interviews, and documentation. The findings indicate that the financial literacy program delivered through interactive learning media effectively improves students’ understanding of basic concepts such as saving, distinguishing between needs and wants, and simple management of pocket money. The program is expected to serve as an initial step in building early financial awareness and encouraging the involvement of schools and families in children’s financial education.

Economic development in rural areas, specifically, aims to improve community welfare. However, in practice, many challenges remain, such as limited technical skills, minimal innovation, and low community capacity to create and develop productive businesses. One way to address these challenges is through human resource empowerment. Gunung Tinggi Village has the potential to develop creative businesses, particularly among housewives, such as dishwashing soap. This product is a household necessity that is used routinely, so demand tends to be sustainable. Therefore, a SWOT analysis was used to provide sustainability recommendations for this business. The methods used included service learning (SL) with a Participatory Action Research (PAR) approach to ensure active participant involvement and the program's alignment with the community's real needs. The results showed that the initial impact of this Community Service Program (PKM) activity was evident in the production of marketable products and the opening of new business opportunities, which directly contributed to the diversification of income sources and increased family economic independence. Furthermore, the application of SWOT analysis proved effective as a tool in formulating sustainability strategies for the micro-enterprises being developed.

The rapid development of digital technology requires greater adaptive capacity in both the economic and educational sectors, including in rural areas. However, limited digital literacy and the continued use of conventional learning methods remain common challenges. This activity aims to enhance the competitiveness of micro, small, and medium enterprises (MSMEs) as well as the quality of primary education in Baharen Village, Sidamanik District, through the implementation of a Community Service Program (Kuliah Kerja Nyata/KKN). A descriptive qualitative approach with a participatory model was employed, actively involving MSME actors, teachers, and students throughout all stages of the program. The activities included assistance in the digitalization of the MSME Gerai Mumtazah through Google Maps registration and the implementation of the QRIS cashless payment system, as well as the application of interactive learning methods based on joyful learning at the Baharen Village State Elementary School. The results indicate an increase in students’ learning motivation and classroom participation, along with improved accessibility and transaction efficiency for the MSME partner. These findings suggest that community-based participatory service activities can make a tangible contribution to strengthening the local economy and improving the quality of education in rural areas.

Teachers’ managerial competence plays a crucial role in ensuring effective and relevant learning, particularly in vocational education. Teachers are required not only to master subject matter but also to plan, manage, and evaluate learning through well-structured and contextual learning tools. This Community Service activity aimed to strengthen teachers’ managerial competence through training on the development of learning tools using a deep learning approach at SMK Muhammadiyah 2 Tangerang Selatan. The implementation method included conceptual material delivery, practical training, and mentoring in designing learning tools. The deep learning approach was applied to encourage learning designs that emphasize deep understanding, critical and reflective thinking, and alignment with workplace demands. The results showed an improvement in teachers’ understanding and skills in developing more systematic, innovative, and adaptive learning tools aligned with 21st-century competencies. This activity contributed positively to strengthening teachers’ roles as learning managers and supporting the improvement of learning quality in vocational schools.

Digital transformation has driven significant changes in organizational management systems, thus demanding adaptive leadership and organizational agility as a key prerequisite for surviving in a dynamic environment. This study aims to analyze the relationship between adaptive leadership and organizational agility in the context of digital transformation through the Systematic Literature Review (SLR) approach. Data were obtained from accredited national journals and relevant international literature, then analyzed using thematic analysis techniques to identify patterns of relationships between variables. The results of the study show that adaptive leadership plays an important role in increasing organizational agility indirectly through strengthening organizational learning and dynamic capabilities. Organizational learning serves as the main mechanism that allows organizations to adapt through the continuous learning process, while dynamic capabilities are the link in transforming learning into strategic responses. Digital transformation accelerates the need for organizations to have a high level of agility in the face of change. In conclusion, organizational agility is the result of systemic processes influenced by adaptive leadership, organizational learning, and dynamic capabilities. This study makes a theoretical contribution to the development of the literature on leadership and strategic management in the digital age as well as practical implications for organizations in improving adaptability to the ever-changing environment.