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The Society 5.0 era brings major changes in various aspects of life, including the economic and financial systems. The integration of digital technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), Big Data, and Financial Technology (Fintech) has created both opportunities and challenges for the development of the sharia economy. This article aims to: (1) analyze the challenges of the sharia economy in the Society 5.0 era, (2) identify opportunities that can be utilized to strengthen the sharia economy, and (3) formulate strategies for strengthening the sharia economy based on digital transformation and the maqashid sharia. This research uses a qualitative approach through literature study (library research) by analyzing various journals, books, reports of sharia financial institutions, and relevant official documents. The results show that the sharia economy faces challenges in the form of low sharia financial literacy, limited human resources, unequal access to technology, and regulations that are not yet fully adaptive to digital developments. However, Society 5.0 also opens up significant opportunities through the development of Islamic Fintech, the digitalization of the halal industry, the optimization of digital zakat and waqf, and the strengthening of Islamic financial inclusion. Therefore, strategies to strengthen the Islamic economy need to be implemented through increasing Islamic digital literacy, developing an Islamic Fintech ecosystem, strengthening Governance based on the principles of Islamic principles (maqasid) and synergy between the government, academia, industry, and the community.

Agentic artificial intelligence (AI) systems capable of autonomous goal-directed behavior, multi-step planning, tool use, multi-agent coordination, and iterative self-correction represent a transition from passive clinical AI tools toward systems that can participate in complex healthcare workflows. However, empirical evidence remains fragmented across clinical decision support, patient monitoring, and administrative applications, and no systematic synthesis has evaluated which agentic principles have been technically demonstrated and which have accumulated sufficient evidence to support responsible clinical deployment. We conducted a PRISMA-informed systematic review of peer-reviewed empirical studies published between January 2025 and April 2026. Searches across five bibliographic databases and Google Scholar, supplemented by citation tracking, identified 443 unique records for screening, of which 25 met the predefined PICOS and quality appraisal criteria. Evidence was synthesized using an evidence-informed seven-principle framework derived from the integration of agentic AI, clinical AI, and healthcare governance literature. This framework provides a structured lens for examining how agentic principles are evaluated individually and in combination, enabling a deployment-readiness perspective that extends beyond capability-focused assessments alone. The evidence base was concentrated on technical capability principles, whereas human oversight, safety, compliance, and equity-related evaluation received comparatively limited attention. Most studies remained at the laboratory, benchmark, or proof-of-concept stage, and none reported demographic-stratified performance outcomes. Overall, the findings suggest a structural asymmetry in agentic healthcare AI: empirical research is advancing agentic capabilities more rapidly than it is generating evidence for the oversight, safety, equity, and governance mechanisms required for responsible clinical translation.

The development of Artificial Intelligence (AI) technology has created various digital innovations, but it has also generated new forms of crime through the misuse of deepfake technology. This study aims to analyze the legal liability of social media platforms for the dissemination of AI-based deepfake content and the forms of legal protection for victims of digital fraud, particularly elderly groups, in the case of the “magical money ritual” scam using the identity of Ujang Busthomi. This research employs normative legal research methods using statutory and case approaches. The results show that perpetrators of deepfake fraud can be held criminally liable under Article 28 paragraph (1) in conjunction with Article 45A paragraph (1) of the Electronic Information and Transactions Law and Article 378 of the Indonesian Criminal Code concerning fraud. In addition, social media platforms as Electronic System Providers also bear preventive and repressive responsibilities under the ITE Law, Government Regulation on Electronic Systems and Transactions, and the Personal Data Protection Law to prevent the spread of illegal content. Legal protection for victims is carried out through criminal law enforcement, personal data protection, restitution mechanisms, and the enhancement of digital literacy in society.

The acceleration of digitalization in recent years has shaped a new socio-economic landscape in Indonesia. Access to app-based financial services and online entertainment has increased rapidly, but at the same time, illegal online lending and digital gambling, targeting adolescents, have flourished. This paper explores a Community Service intervention model that combines preventative digital literacy and artificial intelligence (AI) optimization within the La Tansa Islamic Boarding School in Palembang. The research was conducted using a qualitative approach with participant observation, in-depth interviews, focus group discussions, and written reflection analysis. Findings indicate that strengthening digital literacy based on critical awareness not only improves risk understanding but also deepens self-control integrated with religious values. AI training for educators also encourages changes in learning practices to be more adaptive and reflective of the dynamics of the digital era. This model offers a digital resilience approach that can be replicated in other educational institutions with similar characteristics.

In the era of the Industrial Revolution 4.0 and Society 5.0, digital technology is developing rapidly. This has prompted the world of education to make major changes in learning methods, including in Islamic Religious Education (PAI). Until now, PAI has used outdated methods. These methods are inadequate to meet the needs of children who are accustomed to technology. Therefore, there is a need for technology integration in the learning process. This study aims to determine how to use E-Learning, Learning Management Systems (LMS), and Artificial Intelligence (AI) in PAI learning. This study also aims to determine the benefits, challenges, and strategies for implementing these technologies. This study used a qualitative approach with a literature review type. Data were obtained from various sources, such as books, scientific articles, journals, and relevant documents. The results show that the use of E-Learning, LMS, and AI can improve the quality of PAI learning. E-Learning makes learning easier and more flexible. LMS helps manage learning more systematically. AI helps make learning more personalized and tailored to students' needs. However, the implementation of these technologies still faces several challenges, such as limited infrastructure and low digital skills of teachers and students. Therefore, a strategy is needed to address these challenges. This strategy includes enhancing digital capabilities, strengthening technological infrastructure, developing innovative learning media, and monitoring the use of technology based on Islamic values. This will ensure optimal integration of technology into Islamic Religious Education learning.

The field of education, especially Islamic Religious Education (PAI), has seen substantial changes as a result of the advancement of digital technology and artificial intelligence (AI). The existence of digital media and AI offers numerous opportunities to improve the effectiveness, efficiency, and quality of education through interactive, flexible, and easily accessible learning resources. The purpose of this article is to analyze how artificial intelligence (AI) and digital media are used in Islamic Religious Education. This includes the idea of AI and digital media, AI-based learning materials, the use of AI in educational activities, ethical considerations of AI use, the effects and difficulties of integrating AI, and the Islamic viewpoint on technological advancement. This study employed a library research method by collecting data from relevant journals, books, and other scholarly sources. The findings indicate that the use of AI and digital media in PAI learning can enhance access to information, personalize learning experiences, improve evaluation effectiveness, and increase student engagement in the learning process. However, the implementation of AI also presents several challenges, such as limited digital literacy among teachers, inadequate facilities and infrastructure, ethical concerns, and the potential decline of direct educational interaction between teachers and students. Therefore, the utilization of AI in Islamic Religious Education should be carried out wisely while maintaining Islamic values, ethics, and the primary goal of Islamic education in shaping students’ character.

The rapid growth of digital commerce has encouraged companies to integrate digital and physical marketing channels to create seamless and consistent customer experiences. This study aims to analyze the role of integrated marketing channels through omnichannel strategies, the utilization of Artificial intelligence (AI), and their impact on customer experience in the context of digital commerce. The study employs a Systematic literature review (SLR) approach by examining relevant scholarly articles related to omnichannel marketing, AI technologies, and customer experience. The findings indicate that integrated marketing channels supported by AI enhance service personalization, customer engagement, operational efficiency, and the quality of interactions between companies and customers. Furthermore, the implementation of omnichannel strategies contributes to higher customer satisfaction and loyalty by providing a more connected experience across multiple customer touchpoints. However, the implementation of integrated marketing channels still faces several challenges, including fragmented channel integration, technological complexity, high investment requirements, and concerns regarding customer data privacy and security. Therefore, effective management of integrated marketing channels is essential for improving customer experience while creating sustainable competitive advantages for companies in an increasingly dynamic digital era.

Bullying is a serious problem that can disrupt the learning process and mental development of students, including in Islamic boarding schools. Early detection of bullying is essential to creating a safe and conducive learning environment. This study aims to apply the You Only Look Once (YOLO) algorithm to automatically detect bullying through video recordings in the environment of the SMK Skill Village Islamic School Business Boarding School. The method used involves collecting a video dataset representing various types of bullying behavior, labeling the data, and training an object detection model using the YOLOv5 algorithm. The developed system is capable of detecting and classifying bullying behavior in real- time with detection accuracy reaching [accuracy value if known]. The implementation of this system is expected to assist school authorities and boarding school administrators in monitoring, preventing, and addressing bullying incidents more quickly and effectively, while also serving as an initial step in leveraging artificial intelligence technology to create a safer and more comfortable educational environment.

This community service program addressed the limited capacity of educators to use artificial intelligence in a structured and pedagogically responsible way for textbook development. The program focused on empowering educators through NotebookLM in the Ngabuburit AI 2026 activity. Its objective was to improve participants’ understanding and practical skills in organizing sources, designing textbook structures, developing chapter content, and generating interactive learning media. The program used a participatory training approach consisting of presentation, demonstration, guided practice, discussion, and feedback-based evaluation. The results showed that participants gained a clearer understanding of AI-assisted academic writing workflows and recognized NotebookLM as a useful tool for integrating source analysis, textbook writing, and learning media preparation. The activity also fostered new awareness that artificial intelligence can strengthen, rather than replace, educators’ academic roles in producing more systematic and interactive teaching materials.

The rapid development of Artificial Intelligence (AI) has transformed various sectors of human life, including church ministry and religious organizational management. This study aims to analyze Christian leadership ethics in the use of AI within modern church ministry. The research employs a qualitative descriptive method through theological literature review and analysis of recent studies concerning digital technology and pastoral ministry. The findings indicate that AI provides significant benefits for church administration, digital communication, congregational data management, and online evangelism. Nevertheless, ethical challenges also emerge, such as the weakening of pastoral relationships, data privacy concerns, algorithmic bias, and the risk of dehumanization in ministry practices. From a Christian theological perspective, ministry is fundamentally relational and incarnational, reflecting the example of Jesus Christ who ministered through personal presence, compassion, and direct interaction with people. Therefore, AI should be understood as a supportive instrument rather than a substitute for spiritual authority and pastoral presence. Christian leadership in the digital age must be grounded in integrity, transparency, spiritual discernment, and respect for human dignity as the image of God (imago Dei). This study contributes to the development of ethical guidelines for churches in utilizing AI responsibly while maintaining theological integrity and Christian spiritual values.

Background: Digital transformation in arts education has introduced new paradigms in pedagogical tools, yet the specific role of artificial intelligence in fostering creativity within dance education remains under-explored. Objective: This study aims to analyze the contribution of AI Motion technology to enhancing the creative thinking skills of junior high school students in dance learning. Method: A systematic literature review was conducted following the PRISMA 2020 guidelines. A total of 30 peer-reviewed articles published between 2023 and 2025 were selected from databases including Scopus, ScienceDirect, ERIC, Google Scholar, and Garuda. Data were analyzed using the qualitative descriptive framework of Miles and Huberman. Results: The findings demonstrate that AI Motion technology significantly improves movement accuracy by up to 41% and fosters four key dimensions of creativity: fluency, flexibility, originality, and elaboration. The technology provides real-time feedback and visualization, enabling students to transition from passive imitation to autonomous movement exploration. Conclusion: The integration of AI Motion is strategically aligned with the Indonesian Merdeka Curriculum, which emphasizes differentiated learning and 21st-century skills. However, successful implementation is contingent upon overcoming challenges related to teacher digital competency and infrastructure accessibility. This study provides a conceptual framework for modernizing arts pedagogy through AI-assisted creative exploration.

The rapid advancement of artificial intelligence (AI) has significantly altered the landscape of business decision-making, particularly for Micro, Small, and Medium Enterprises (MSMEs) in Indonesia. Despite their critical role in economic growth and employment generation, MSMEs frequently encounter substantial barriers related to technological adoption, digital literacy, and resource allocation. This research systematically investigates the utilization of artificial intelligence as a responsive innovation mechanism designed to optimize decision-making processes within the MSME sector. By employing a comprehensive systematic literature review methodology, analyzing peer-reviewed publications from globally recognized academic databases between 2018 and 2024, this study delineates the multifaceted benefits, complex challenges, and contextualized adoption strategies associated with AI integration. The findings reveal that artificial intelligence holds transformative potential for enhancing operational efficiency, refining predictive analytics, and sustaining long-term competitiveness. Conversely, critical challenges, including inadequate digital infrastructure, financial constraints, and organizational resistance, remain pervasive obstacles. Consequently, the study advocates for a gradual, responsive innovation approach that aligns technological adoption with specific business needs and environmental pressures. The insights derived from this research offer substantial theoretical and practical implications, providing a strategic framework for policymakers and MSME owners to accelerate digital transformation inclusively.

.This study aims to analyse the role of Financial Information Systems (FIS) in supporting risk management, decision-making, and organisational performance in the digital transformation era. This study employs the Systematic Literature Review (SLR) method to examine articles indexed in Scopus from 2016 to 2026. The PRISMA framework is used to ensure a systematic, transparent article selection process, resulting in the selection of 37 relevant articles for further analysis. The results of the study show that Financial Information Systems make a major contribution to improving financial transparency, operational efficiency, the quality of strategic decision-making, and organisational risk mitigation. In addition, the integration of emerging technologies such as Artificial Intelligence (AI), FinTech, big data analytics, and cloud computing further strengthens the effectiveness of financial information systems in modern organisations. This study contributes theoretically by mapping research trends and identifying research gaps, while providing practical benefits for organisations seeking to increase competitiveness through digital financial systems. For future research, it is recommended to develop a more predictive and intelligent Financial Information Systems model to address future business dynamics.

Digital transformation has positioned artificial intelligence (AI) as a major driver of organizational change and innovation. This study aims to analyze the influence of AI implementation on transformational leadership dynamics and the shifting role of leaders in managing human resources through a Systematic Literature Review of reputable studies published within the last five years. The findings indicate that AI acts as a catalyst in strengthening the dimensions of intellectual stimulation and individualized consideration through predictive analytics and talent personalization. The automation of administrative and repetitive tasks enables leaders to focus more on strategic vision, organizational innovation, decision-making, and emotional engagement with employees. However, the effectiveness of AI implementation is highly dependent on leaders’ digital literacy, adaptive capabilities, and readiness to integrate technology into organizational processes. This study contributes by proposing a hybrid leadership framework that combines artificial intelligence with human emotional intelligence to support more effective leadership practices. The practical implications emphasize the importance of leadership development that prioritizes empathy, ethical awareness, and algorithmic transparency in order to maintain trust, encourage sustainable innovation, and strengthen organizational resilience in increasingly dynamic and volatile environments.

Artificial intelligence has the potential to support radiology workflows by assisting in the identification of cases that may require additional clinical attention. However, alert-oriented medical AI systems should provide not only classification outputs but also interpretable evidence that can be reviewed and audited by clinicians. This study develops and evaluates an explainable multimodal framework for binary chest X-ray alert classification using paired radiology reports and chest X-ray images. The text branch employs TF-IDF n-gram features with a class-balanced Logistic Regression classifier, while the image branch fine-tunes a pretrained ResNet18 model. The two branches are integrated through probability-level late fusion using a validation-selected fusion weight. Explainability is implemented in a modality-specific manner: global coefficient analysis is used to identify influential textual cues, while Grad-CAM heatmaps are used to visualize salient image regions. Experiments were conducted on paired samples from the Open-i/IU X-Ray dataset using text-only, image-only, and fusion-based evaluation settings. Additional analyses include case-level complementarity analysis, bootstrap confidence intervals for ROC-AUC, shortcut-feature inspection, and qualitative Grad-CAM auditing. The results indicate that the text modality provides the dominant predictive signal under the current proxy-label setting. Late fusion produced a small descriptive improvement on the test set, increasing accuracy from 0.8533 to 0.8667, F1-score from 0.8817 to 0.8936, and ROC-AUC from 0.8936 to 0.9025 compared with the text-only baseline. However, the observed ROC-AUC improvement was not statistically conclusive based on bootstrap analysis. These findings suggest that the proposed framework is useful as a reproducible and auditable multimodal prototype, while also highlighting important limitations, including proxy-label ambiguity, potential label leakage from radiology reports, limited image-branch contribution, lack of external validation, and the need for stronger explanation and calibration assessment.

Student dropout in higher education remains a persistent socioeconomic challenge, yet many predictive models reported in the literature are methodologically compromised by randomized cross-validation schemes that introduce temporal data leakage and artificially inflate predictive performance. This study proposes a longitudinal prescriptive learning analytics framework integrating three complementary methodological components: a Leave-One-Cohort-Out (LOCO) temporal validation protocol, a hybrid SMOTE-ENN class balancing strategy, and temporal velocity feature engineering derived from Learning Management System (LMS) behavioral trajectories. The framework was evaluated on a longitudinal dataset comprising 464,739 enrollment records and 77 features. Five predictive algorithms—XGBoost, LightGBM, CatBoost, Random Forest, and Logistic Regression—were comparatively assessed on a strictly isolated blind holdout cohort (2022), with CatBoost emerging as the champion estimator, achieving a PR-AUC of 0.8859, a Macro F1-Score of 0.9143, and the lowest Brier Score (0.0221), thereby demonstrating superior calibration and discriminative capability under severe class imbalance (93:7 ratio). Comprehensive ablation analysis revealed that temporal velocity features function not merely as additive predictors, but as a structural prerequisite enabling Synthetic Minority Oversampling Technique with Edited Nearest Neighbors (SMOTE-ENN) to generate high-quality synthetic boundary instances; removing these features reduced minority-class precision from 0.8302 to 0.6721. To operationalize predictive outputs into actionable intervention pathways, Diverse Counterfactual Explanations (DiCE) were implemented under a three-tier causal constraint architecture on 96 borderline high-risk students, generating 384 feasible intervention scenarios exclusively targeting forward-looking behavioral velocity metrics without constraint violations. Collectively, these findings advance the paradigm of prescriptive learning analytics by providing educational institutions with interpretable risk diagnostics and operationally feasible intervention guidance grounded in empirically validated behavioral and temporal dynamics.

The development of digital technology and artificial intelligence (AI) has increased youth interaction in cyberspace, while also elevating the risk of digital crimes, both as perpetrators and victims. This community service program AIms to strengthen legal awareness and AI-based self-protection skills among students of SMKN 1 Kaligondang, \Purbalingga Regency. The implementation methods include interactive workshops, digital security trAIning, case simulations, and the use of a mini AI assistant as a preventive educational tool. Evaluation was conducted through pre-test and post-test assessments, participatory observation, and participant reflection. The results indicate an improvement in participants’ understanding of digital law, particularly regarding the provisions of the Law on Electronic Information and Transactions and its relevance to the Indonesian Criminal Code. In addition, participants’ technical skills in securing digital accounts, identifying phishing attempts, and responding to cyber risks have also improved. The use of AI has proven to enhance participation and contextual understanding. Overall, the program is effective in fostering legal awareness, improving digital protection capabilities, and shaping responsible behavior in cyberspace.

The advancement of Artificial Intelligence (AI) has brought significant changes to various aspects of life, including the field of education, particularly Islamic Education. This study aims to explore various AI-based learning strategies that can be implemented to create a more effective, interactive, and contemporary learning process. The method employed in this study is a literature review by examining various scholarly sources, such as journals, books, and academic articles discussing the implementation of AI in education. The findings indicate that the integration of AI can be realized through personalized learning tailored to students’ needs, the use of chatbots as learning assistants, the analysis of students’ progress and learning outcomes, as well as the utilization of technology-based learning media. These strategies are not only capable of improving students’ cognitive aspects but can also strengthen the internalization of Islamic values when applied appropriately and systematically. Furthermore, this study emphasizes that teachers’ technological competence, infrastructure readiness, and the development of an adaptive curriculum are crucial factors in the successful implementation of AI-based learning within the context of Islamic Education.

In the field of education, cheating remains a critical issue of academic dishonesty. This phenomenon does not merely affect students' grades but also obstructs the development of core character values, including integrity, responsibility, and independence in the pursuit of knowledge. This article aims to analyze the factors influencing cheating behavior and evaluate various forms of intervention from the perspective of early detection of problematic behavior. This study employs a literature review method, examining 20 relevant scientific articles published between 2021 and 2026. The findings reveal that cheating behavior is influenced by internal factors such as self-efficacy, self-control, self-confidence, religiosity, and self-esteem as well as external factors, including peer conformity, parental pressure, the learning environment, and evaluation systems. Effective interventions include group counseling services, classical guidance, modifications to evaluation systems, and the utilization of Artificial Intelligence-based technology to detect potential academic fraud. Consequently, efforts to prevent cheating behavior require a multidimensional approach involving students, teachers, parents, and school policies to cultivate an academic culture of integrity.

This study aims to examine the application of artificial intelligence (AI) in learning to write scientific papers and its impact on the level of originality of students in the Indonesian Language and Literature Education Study Program (PBSI) Semester VI at Bina Bangsa University. This study uses a qualitative method with a descriptive approach, which was conducted through reference searches and interviews with 30 of the total 35 students. The results of the study indicate that AI is utilized functionally as an aid in the writing process, such as compiling outlines, paraphrasing, developing ideas, and reviewing writing. Based on the interview results, the use of AI platforms is dominated by Chat GPT at 50%, followed by Perplexity at 27%, and Gemini.ai at 23%. These findings indicate that students do not rely on a single platform, but rather utilize various technologies adaptively according to their needs. In terms of discussion, the use of AI has a positive impact in the form of improved writing quality, time efficiency, development of creative ideas, increased digital literacy, and the provision of instant feedback. However, there are also challenges that arise, such as the potential for decreased originality, dependence on AI, the risk of violating academic ethics, information bias, and data privacy issues. Therefore, AI should be used as a tool, not a substitute for students' critical thinking skills. This research emphasizes the importance of using AI wisely, critically, and responsibly so that students can produce original, high-quality, and integrated scientific work in the digital age.