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Artificial Intelligence (AI) is increasingly recognized as a transformative enabler of early disease detection, with the potential to improve diagnostic accuracy, support predictive risk stratification, and advance preventive healthcare. Despite rapid methodological progress, many existing reviews remain performance-centric, offering limited insight into generalizability, ethical governance, and real-world implementation constraints. This paper presents a narrative and integrative review with an adoption-focused, translational perspective, synthesizing recent developments in AI-driven early disease detection across oncology, cardiology, neurology, and infectious disease surveillance. Drawing on peer-reviewed literature published primarily between 2016 and 2025, the review examines reported performance gains alongside persistent limitations related to data heterogeneity, population bias, explainability, and regulatory fragmentation. Through cross-sectional synthesis, we identify three recurring gaps in prior reviews: (i) overgeneralization of AI’s diagnostic superiority, (ii) insufficient consideration of ethical and legal accountability, and (iii) a lack of actionable guidance for scalable clinical implementation. Integrating technical, ethical, and policy dimensions into a unified conceptual framework, this review demonstrates that while AI systems can consistently enhance diagnostic accuracy and early risk stratification in well-defined tasks, sustained clinical adoption depends on aligning technical performance with governance readiness, interpretability, and workflow integration. The analysis further highlights how implementation mechanisms—such as explainable AI, continuous post-deployment monitoring, and clinician-centered deployment strategies—mediate the translation of algorithmic innovation into real-world healthcare impact. Overall, this review provides a critical reference for researchers, clinicians, and policymakers seeking to translate AI innovation into safe, equitable, and trustworthy clinical practice.

This study developed and evaluated an AI-integrated Virtual Reality (VR) system designed to enhance personalized learning in higher education. While VR improves engagement, existing systems often lack adaptivity or experience high latency during AI interactions. To address these limitations, this research introduces a novel integration of a cache-optimized Llama 2 Large Language Model (LLM) that delivers real-time, motivationally grounded feedback. The system was implemented using Unity 3D and validated with 50 undergraduate students. Technical validation showed that the cache layer reduced interaction latency from 17.7 ms to 14.2 ms and maintained zero system crashes throughout the pilot. Learner motivation was assessed using Keller’s ARCS model, yielding mean scores ranging from 4.08 to 4.69 across all dimensions. Independent t-tests (p > 0.05) and negligible effect sizes (Cohen’s d < 0.2) revealed no significant difference between technical (ICT) and non-technical (Physics) students. These findings confirm that the proposed system effectively bridges technological and motivational gaps, providing a robust model for adaptive, immersive education.

Prompt injection has emerged as a critical security threat for Large Language Models (LLMs), exploiting their inability to separate instructions from data within application contexts reliably. This paper provides a structured review of current attack vectors, including direct and indirect prompt injection, and highlights the limitations of existing defenses, with particular attention to the fragility of Known-Answer Detection (KAD) against adaptive attacks such as DataFlip. To address these gaps, we propose a novel, hybrid, multi-layered detection framework that operates in real-time. The architecture integrates heuristic pre-filtering for rapid elimination of obvious threats, semantic analysis using fine-tuned transformer embeddings for detecting obfuscated prompts, and behavioral pattern recognition to capture subtle manipulations that evade earlier layers. Our hybrid model achieved an accuracy of 0.974, precision of 1.000, recall of 0.950, and an F1 score of 0.974, indicating strong and balanced detection performance. Unlike prior siloed defenses, the framework proposes coverage across input, semantic, and behavioral dimensions. This layered approach offers a resilient and practical defense, advancing the state of security for LLM-integrated applications.

The integration of Artificial Intelligence (AI) into precision agriculture has significantly improved plant disease recognition; however, many existing deep learning models remain computationally expensive and feature-redundant, limiting their deployment on low-power and edge devices. To address these limitations, this study proposes a lightweight framework for maize leaf disease recognition based on serial deep feature extraction, dimensionality reduction, and machine-learning–based classification. A pre-trained MobileNetV2 network is employed as a fixed feature extractor to obtain discriminative visual representations, while Principal Component Analysis (PCA) is applied to reduce feature dimensionality by approximately 76%, retaining 95% of the original variance and improving computational efficiency. The compressed features are subsequently classified using a Radial Basis Function Support Vector Machine (RBF-SVM), optimized via grid search and cross-validation. Experiments conducted on a four-class maize leaf disease dataset (Northern Leaf Blight, Common Rust, Gray Leaf Spot, and Healthy), with class imbalance handled during training, demonstrate that the proposed MobileNetV2–PCA–SVM pipeline achieves 97.58% accuracy, 96.60% precision, 96.59% recall, and 96.59% F1-score, outperforming the DenseNet201 + Bayesian-optimized SVM baseline (94.60%, 94.40%, 94.40%, and 94.40%, respectively). This improvement corresponds to a 2.98% accuracy gain, a 55% reduction in error rate, an 86% reduction in model parameters (20.31M to 2.75M), and an 85% reduction in model size (81 MB to 12 MB). These results indicate that the proposed framework provides a compact and efficient solution with strong potential for deployment in resource-constrained agricultural environments.

The maritime education sector faces complex challenges in preparing competent seafarers amid the rapid advancement of digital technology. This study investigates the effect of artificial intelligence-based simulations and AI-based competency assessments on competency achievement levels among nautical cadets at Indonesian maritime training institutions. The research design employed a convergent parallel mixed-methods approach, integrating quantitative and qualitative methods to gain a comprehensive understanding. Quantitative data were collected from 150 cadets using a validated questionnaire. In comparison, qualitative data were obtained through in-depth semi-structured interviews with fifteen instructors and ten cadets. Multiple regression analysis revealed that the research model significantly predicted cadet competency achievement. The findings indicate that AI-based assessments exert a stronger influence than AI simulations in improving competency. The qualitative exploration highlighted adaptive feedback mechanisms and personalized learning pathways as critical success factors in implementing learning technologies. This study provides empirical evidence for maritime institutions to prioritize strategic investments in AI-based assessment systems while maintaining a human-centered pedagogy. The research contribution lies in integrating fourth industrial revolution technologies into the training, certification, and watchkeeping standards compliance framework for seafarers, thereby strengthening Indonesia's maritime education ecosystem and aligning it with international standards.

This study examines the construction of heroism in the character of Joshua Taylor in The Creator (2023) using Christopher Vogler’s Hero’s Journey framework. In modern cinema, heroism is no longer limited to physical strength or traditional bravery but is often defined through moral struggle, emotional conflict, and ethical decisions in complex situations. Joshua represents a modern hero whose journey unfolds amid a global conflict between humans and artificial intelligence (AI). This research applies a qualitative descriptive method with a structural mapping technique to analyze Joshua’s narrative progression across the stages of Vogler’s Hero’s Journey. The findings show that Joshua’s heroism is constructed through moral transformation, sacrifice, and his shift from a disillusioned soldier to a protector figure who prioritizes life and empathy over ideology. His journey highlights that modern heroism is rooted in ethical awareness and personal redemption rather than dominance or power, it reflects cinematic developments in depicting heroism that are more relevant to today's social and technological context.

The rapid growth of short-form social media platforms has increased the complexity of decision-making during the digital content planning stage. Content creators are required to evaluate the feasibility of content ideas and determine platform suitability prior to production, while most existing tools primarily focus on post-publication analytics. This study aims to design an Artificial Intelligence (AI)-enabled Decision Support System (DSS) to evaluate digital content ideas in the pre-production stage. Adopting a Design Science Research approach, the study develops a conceptual design artifact that integrates intrinsic content idea characteristics with cognitive and affective response modeling grounded in the Stimulus–Organism–Response (S-O-R) framework, alongside platform affordance mapping. The proposed artifact operationalizes a reflective evaluation mechanism that generates platform recommendation scores and idea enhancement suggestions without claiming deterministic or predictive performance modeling. Evaluation was conducted qualitatively through practitioner assessment to examine perceived usefulness, clarity of recommendations, and decision support contribution. The findings indicate that the developed artifact provides a structured reflective framework for early-stage content evaluation. Theoretically, this study extends the application of the S-O-R framework by operationalizing it as a design logic for a pre-production DSS artifact. Practically, the proposed system has the potential to support more systematic decision-making prior to content production.

Open-pit mining activities in Indonesia contribute significantly to the national economy but require stringent monitoring to mitigate environmental degradation. Conventional monitoring methods relying on terrestrial surveys are often constrained by vast coverage areas, high operational costs, and limited field accessibility. This study aims to develop an artificial intelligence model capable of automatically detecting and mapping mining areas to enhance surveillance efficiency. The applied method is Deep Semantic Segmentation utilizing the U-Net Convolutional Neural Network (CNN) architecture. The model was trained using Sentinel-2 satellite imagery, focusing exclusively on Red, Green, and Blue (RGB) spectral channels to replicate human visual perception. Experimental results demonstrate that the proposed model performs reliable segmentation of mining areas, achieving an Accuracy of 93.58% and a Global Intersection over Union (IoU) of 0.8067. These findings indicate that the U-Net architecture can effectively extract spatial features of mines even when utilizing standard visual data. This research contributes to the development of an efficient, cost-effective, and scalable digital monitoring prototype to support innovation in sustainable environmental governance.

This study analyzes the relationship between Christian faith and artificial intelligence (AI) through a theological-philosophical approach and assesses its relevance to the existence of the Catholic Church in the digital age. The background of this study stems from the rapid development of AI, which brings both opportunities and risks to the life of faith, such as the simplification of theological teachings, the reduction of personal relationships, and the emergence of a technocratic paradigm. The study uses qualitative methods with a literature review of the Holy Scriptures, Church documents, and literature on philosophy and technology ethics. The results of the analysis show that AI is a product of human creativity as the image of God, so it does not conflict with faith, but it remains instrumental and does not have moral or spiritual dimensions like humans. Therefore, AI cannot replace the role of humans in faith relationships. The Church is called to guide technological development through Christian ethical principles, upholding human dignity, being critical of the effects of dehumanization, and utilizing AI wisely for evangelization, catechesis, and faith education. With a reflective and critical approach, the Church can remain relevant amid technological advances without losing its identity and mission for the common good.  

The industrial revolution 4.0 has brought rapid advances in technology, one of which is artificial intelligence (AI). AI has the ability to imitate the human thought and action process in solving various problems. However, the implementation of AI raises legal problems related to responsibility for the negative impacts caused, such as cybercrime, information manipulation, privacy violations, and misuse of technology. Indonesia, as a country based on law, is faced with the challenge of regulating AI to be in line with technological developments. Currently, legal regulations in Indonesia do not specifically regulate the legal responsibility of AI. Positive laws, such as the Civil Code and the ITE Law, can be used interpretively, but are not enough to address the complexity of AI. Legal responsibility related to AI is debatable, whether it is imposed on the developer, owner, or user of AI. In addition, AI does not have a “mens rea” in criminal law, so that unlawful acts are more relevant to be imposed on the responsible human. To overcome this problem, legal reform or the creation of special regulations that comprehensively regulate AI are needed. These regulations must include privacy protection, data security, and criminal and civil liability due to the use of AI. With a clear legal framework, the risk of AI misuse can be minimized and its use can be optimized for the welfare of society.

Financial distress is a condition of declining financial health of a company that can develop gradually and lead to business failure if not detected early. With the increasing complexity of the business environment and the limitations of conventional statistical methods, Artificial Intelligence/AI is increasingly being adopted in the development of early warning systems (EWS) to predict financial distress. This study aims to examine the development of AI-based EWS research, identify the most widely used algorithms, and evaluate the effectiveness of AI models compared to conventional methods in predicting financial distress. The method used is a comprehensive systematic literature review of 15 relevant scientific articles. The results show that the paradigm has shifted from statistical models to machine learning and deep learning. Random Forest and Artificial Neural Network are the most widely used algorithms and have better predictive performance. This study offers a conceptual synthesis of the progress, effectiveness, and challenges of applying AI in predicting financial distress and opens opportunities for further research on the development of contextual and interpretative EWS.

The rapid development of Artificial Intelligence (AI) technology presents significant opportunities to improve the quality of learning; however, its utilization in school settings is still constrained by teachers’ limited competencies. This community service program aims to enhance teachers’ competencies in utilizing AI-based learning technologies in a pedagogical, ethical, and practical manner. The implementation method consisted of preparation, implementation, and evaluation stages. The activities were carried out through interactive workshops, demonstrations of AI-based educational tools, hands-on practice with mentoring, and evaluation using pre-tests and post-tests. The results indicate a significant improvement in teachers’ understanding and skills in using AI for lesson planning, instructional media development, and learning assessment. Teachers also demonstrated more positive attitudes and greater confidence in integrating AI into classroom practices. Nevertheless, challenges such as varying levels of digital literacy and limited technological infrastructure were still identified. Overall, this community service activity proved effective in improving teachers’ competencies and has the potential to support more innovative and adaptive learning transformation in the digital era.

The advancement of Artificial Intelligence (AI) presents substantial potential for improving the quality of learning in elementary education, particularly through the application of AI prompts as instructional support in lesson planning and classroom implementation. Nevertheless, many teachers at Integrated Islamic Elementary Schools (Sekolah Dasar Islam Terpadu/SDIT) experience limited AI literacy and insufficient competence in designing pedagogically appropriate and effective AI prompts. This community service program aims to enhance AI literacy and strengthen the professional competence of teachers at SDIT Lukmanul Hakim Puring, Kebumen, in utilizing AI prompts to support instructional design, teaching materials development, and learning assessment. The program employed participatory methods, including socialization sessions, hands-on training, guided practice, and continuous mentoring. The results demonstrate a measurable improvement in teachers’ understanding and practical skills in constructing AI prompts aligned with pedagogical objectives. Furthermore, the integration of AI prompts contributes to increased instructional efficiency and pedagogical creativity. This program is expected to provide a sustainable model for supporting digital transformation in elementary school education.

This study aims to examine the legal certainty of ownership of works produced by artificial intelligence (AI), specifically ChatGPT, from the perspective of Law Number 28 of 2014 concerning Copyright. The main focus of this research is to answer the question of whether works produced by AI can be copyrighted and to identify the legal challenges arising from the absence of explicit regulations in the Indonesian positive legal system. This study uses a normative juridical method with a statute approach and analysis of primary and supplementary legal materials. The study's findings indicate that, to date, there are no national regulations explicitly governing copyright recognition for works produced autonomously by AI systems. Based on the provisions of Article 1 number 3 of Law Number 28 of 2014, works must arise from human intellectual ability, therefore, AI products do not qualify as works potentially entitled to copyright protection. Therefore, legal reformulation through regulatory updates is needed to provide legal certainty and address challenges in the digital era, as well as prevent potential disputes in the national creative industry.

The rapid development of artificial intelligence technology, particularly deepfake, poses significant challenges to legal protection due to its potential misuse for identity manipulation, defamation, and other cybercrimes. This phenomenon highlights a gap between technological advancement and the readiness of legal regulations in Indonesia. This study aims to analyze forms of deepfake misuse and to assess the effectiveness of existing legal frameworks in providing legal protection and certainty. The research employs a normative legal method using statutory and conceptual approaches by examining legislation, legal doctrines, and relevant scholarly literature. The findings indicate that Indonesian positive law does not yet specifically regulate deepfake technology, resulting in law enforcement relying on general provisions of criminal law and the Electronic Information and Transactions Law. The implications of this study emphasize the urgency of regulatory reform and the formulation of adaptive legal policies to address digital technological developments in order to ensure legal protection and justice for society.

The rapid advancement of Artificial Intelligence (AI) has significantly disrupted the global labor sector, including in Indonesia. The urgency of this study lies in the growing inequality in access to digital skills and the lack of legal protection for workers in the digital era. This research aims to analyze the impact of AI on employment in Indonesia and to assess the adequacy of labor regulations in addressing digital transformation. This study employs a normative legal method with a juridical-empirical approach, utilizing literature review, secondary data, and qualitative analysis of labor policies and relevant regulations. The findings reveal that while AI has the potential to create new types of employment, it also threatens conventional jobs, especially in labor-intensive sectors. Moreover, Indonesia's labor regulations have not yet adapted to new, flexible, and platform-based work models, resulting in legal uncertainty for informal and freelance workers. This study contributes to the discourse on the need for labor law reform that is inclusive and adaptive to technological developments. In conclusion, there is a pressing need for responsive labor regulation reform, increased digital literacy, and continuous reskilling systems to ensure that AI-driven transformation does not create new inequalities in the labor market. Future research is recommended to focus on formulating new legal protection models for digital workers in the AI era.

The development of deepfake technology, which utilises artificial intelligence to manipulate images, videos and sounds, has led to a serious threat of sextortion. In the Indonesian context, high internet penetration and low awareness of digital security increase the risk of this crime. This research analyses the legal consequences of the misuse of deepfake technology in sextortion based on the Electronic Information and Transaction Law (UU ITE). Using normative juridical methods and descriptive-qualitative analysis, this research examines the legal challenges faced in enforcing sanctions against this crime and provides recommendations to strengthen the legal framework in Indonesia. The results show that there are gaps in the legal framework that need to be addressed, as well as the importance of education and capacity building of law enforcement in dealing with cybercrime. In addition, the development of more sophisticated deepfake detection technology is expected to be a solution in tackling this abuse in the future.

The rapid advancement of Artificial Intelligence (AI) is reshaping public governance, including legislative processes. In the United Arab Emirates (UAE), AI is being actively utilized to enhance law-making through faster drafting, improved consistency, and greater transparency. This study examines the role of AI in the UAE’s legislative functions, focusing on how AI tools assist in analyzing legal data, formulating policy recommendations, and drafting legislation. It explores how AI impacts the speed, accuracy, and legitimacy of law-making, while also addressing the ethical and legal challenges of delegating legislative tasks to intelligent systems. Using a qualitative case study method, the paper evaluates government initiatives, expert insights, and regulatory structures that frame AI's integration into the UAE’s law-making system. While AI offers opportunities for data-driven governance and increased legislative productivity, it also presents risks such as algorithmic bias, reduced human oversight, and accountability gaps. The study emphasizes that AI must be governed by strong regulatory frameworks to safeguard democratic values, fairness, and legal integrity. By analyzing a pioneering national model, this research contributes to global discussions on AI in governance and offers key insights for policymakers, technologists, and legal scholars seeking to balance innovation with ethical and legal standards.

Digital transformation in the public sector has encouraged the adoption of Artificial Intelligence (AI) as a strategic instrument to enhance the effectiveness and quality of public service delivery. In Indonesia, the implementation of AI within the public administrative system remains at an early stage and faces various structural, regulatory, and ethical challenges. This study aims to analyze the opportunities, challenges, and ethical implications of AI implementation in Indonesia’s public administration. The research employs a qualitative approach through literature review and policy analysis of governmental digital transformation regulations. The findings indicate that AI holds significant potential to improve bureaucratic efficiency, service transparency, and data-driven decision-making processes. However, regulatory gaps, limited digital literacy among public officials, the risk of algorithmic bias, and data protection concerns constitute major obstacles to its effective implementation. The novelty of this study lies in integrating public administration management analysis with a public service ethics framework grounded in good governance principles within the context of AI implementation in Indonesia. This study recommends strengthening regulatory frameworks for AI in the public sector, enhancing human resource capacity, and developing ethical guidelines for AI utilization to ensure that public services remain accountable, equitable, and oriented toward the public interest.

The development of artificial intelligence technology is a great opportunity for the fashion industry, especially in designers based on personalization and consumer needs. This study aims to examine Midjourney's AI technology in the design personalization process by integrating solid data and consumer style preferences. This research is expected to support the concept of mass customization in the fashion industry and increase the relevance of design to user character. This research uses a mixed method method by combining quantitative data and qualitative data. The research stages include body data collection and style preferences, prompt formulation, data-driven prompt formulation, design generation using Midjourney, design validation by experts and consumers, and integrated data analysis.The results showed that the majority of the designs produced were considered feasible in terms of construction (83%) and in accordance with the character of the consumer's body (75%). The modest and minimalist style categories received the highest personalization scores. The qualitative findings reinforce the quantitative results, showing that consumers feel the fit of the style and proportions of the design with the character of their bodies.The study concludes that Midjourney's AI integration in the fashion design process is able to effectively support design personalization, although it still requires the role of designers in technical refinement. This approach has the potential to be an innovative solution in the development of data-driven fashion design.