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Image Quality Assessment has become an important research area in computer vision due to the increasing use of digital imaging in medical, industrial, and remote sensing applications. This study conducted a Systematic Literature Review (SLR) using articles retrieved exclusively from the Scopus database. A total of fifteen Scopus-indexed articles were selected through the PRISMA process consisting of identification, screening, eligibility, and inclusion stages. The findings indicated that convolutional neural networks, transformer architectures, and no-reference approaches dominated recent studies because of their ability to capture complex visual features and improve evaluation accuracy. The application domain strongly influenced method selection, particularly in medical imaging, industrial quality control, and remote sensing. Deep learning methods achieved high performance but required large datasets and high computational resources. Overall, recent research trends shifted from traditional image enhancement toward artificial intelligence-based quality evaluation.

This review explores the integration of artificial intelligence in higher education, focusing on its impact on teaching and learning, barriers to adoption, and strategies for sustainable implementation. A narrative review of peer-reviewed literature published from 2023 to 2026 was conducted, emphasizing student and educator perspectives. The findings show that AI improves learning efficiency, supports personalized learning, and enhances academic writing, while also introducing concerns related to academic integrity, algorithmic bias, AI colonialism, and limited formal training. The review suggests that successful adoption depends on institutional support, AI literacy, ethical policy frameworks, and cross-disciplinary professional development. Generative AI should serve as an enhancer rather than a replacement in human-centered education.

Pesatnya kemajuan teknologi digital telah mempercepat adopsi *Internet of Things* (IoT) dalam berbagai aspek kehidupan sehari-hari. IoT memungkinkan perangkat fisik untuk berkomunikasi dan bertukar data melalui konektivitas internet, sehingga proses menjadi lebih otomatis, efisien, dan responsif. Studi ini bertujuan untuk menganalisis pemanfaatan IoT dalam meningkatkan efisiensi kehidupan sehari-hari melalui pendekatan tinjauan pustaka. Metode penelitian yang digunakan adalah *Systematic Literature Review* (SLR), yang mengkaji dan menyintesis temuan dari berbagai studi terdahulu mengenai penerapan IoT di berbagai sektor, seperti rumah pintar (*smart home*), layanan kesehatan, transportasi, pertanian, dan pemantauan lingkungan. Hasil analisis menunjukkan bahwa IoT berkontribusi signifikan terhadap peningkatan efisiensi operasional, penghematan energi, perbaikan proses pengambilan keputusan, serta peningkatan kenyamanan pengguna. Terlepas dari berbagai manfaat tersebut, masih terdapat sejumlah tantangan, termasuk keamanan data, masalah privasi, interoperabilitas antarperangkat, biaya implementasi, dan ketergantungan pada infrastruktur internet yang stabil. Temuan studi ini menunjukkan bahwa IoT telah menjadi salah satu teknologi kunci yang mendukung transformasi digital dan peningkatan kualitas hidup. Pengembangan di masa mendatang diharapkan berfokus pada penguatan aspek keamanan, perluasan standar interoperabilitas, dan integrasi kecerdasan buatan (*artificial intelligence*) guna memaksimalkan efektivitas penerapan IoT dalam aktivitas sehari-hari.  

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.

The development of Artificial Intelligence (AI) has driven significant transformation in hospital management, particularly in operational efficiency, service quality, and patient safety. This study aims to analyze the implementation of AI in hospital management based on recent scientific evidence from 2020 to 2026. The method used was a systematic review guided by the PRISMA 2020 framework. Literature was retrieved from PubMed, ScienceDirect, SpringerLink, Google Scholar, and ProQuest. From 360 identified articles, a stepwise selection process was conducted, resulting in 15 articles that met the inclusion criteria. The findings indicate that AI contributes to improved operational efficiency through patient flow optimization, operating room management, workforce scheduling, and electronic medical record management. AI also enhances service quality through predictive data analytics and supports patient safety through risk detection and early warning systems. In conclusion, AI has strong strategic potential to support modern hospital management. However, its implementation still faces several challenges, including human resource readiness, data security, algorithmic bias, system interoperability, and investment requirements. Therefore, AI implementation should be carried out in a planned, ethical manner and evaluated from a health economics perspective.

This study aims to analyze the effect of artificial intelligence (AI)-based demand forecasting and big data analytics on inventory optimization through prediction accuracy among e-commerce actors or marketplace sellers in Curug. This research employed an explanatory quantitative approach involving 100 respondents selected through purposive sampling based on predefined criteria relevant to the research objectives. Data were collected using a structured Likert-scale questionnaire and analyzed using Structural Equation Modeling Partial Least Squares (SEM-PLS) by evaluating the measurement model, structural model, and mediation effects. The findings reveal that AI-based demand forecasting and big data analytics have a positive and significant effect on prediction accuracy. Furthermore, prediction accuracy has a positive and significant effect on inventory optimization and significantly mediates the relationship between AI-based demand forecasting, big data analytics, and inventory optimization. These findings indicate that the integration of AI and big data analytics contributes to more accurate demand prediction, leading to improved inventory management performance. The implication of this study suggests that e-commerce actors should improve data quality, analytical capability, and the adoption of predictive technologies to support more accurate, efficient, and responsive inventory decisions, thereby enhancing operational performance and competitiveness in responding to dynamic market demand.      

The community service activities aim to improve business financial management skills and student readiness in facing the 2026 Indonesian Student Innovation and Entrepreneurship Festival (FIKSI). The program is implemented by lecturers of the Digital Business Study Program at Al-Irsyad University Cilacap together with supervising teachers and 26 FIKSI finalist students from SMA Negeri 2 Cilacap, SMA Negeri 3 Cilacap, and SMA Negeri 1 Maos. The main problem faced by partners is the low ability to prepare business financial reports and the use of digital technology in business development. The method used is Participatory Action Research (PAR) through stages of socialization, training, technology implementation, mentoring, evaluation, and program sustainability. The results of the activity show an increase in student understanding and skills in recording transactions, calculating the cost of production, preparing profit and loss and cash flow reports, and utilizing digital financial applications and Artificial Intelligence (AI) to support business development. In addition, supervising teachers received capacity building in assisting students in aspects of financial management and preparing business proposals. The establishment of the Smart Fintech Young Entrepreneur Community is a sustainable strategy for the program to support the development of digital entrepreneurship in schools. This program has proven effective in improving the quality of business and improving students' readiness for entrepreneurship competitions.

Artificial Intelligence (AI) has increasingly influenced higher education, including learning practices among students of Islamic Education (PAI). Applications such as ChatGPT, Claude AI, and Gemini are widely used to support academic activities, including information searching, understanding course materials, idea generation, and assignment completion. This study aims to examine the use of AI among PAI students and analyze its implications for critical thinking skills. A qualitative descriptive approach was employed at the Islamic Education Program of UIN Salatiga. Data were collected through written interviews with 12 students selected using purposive sampling and analyzed using an interactive data analysis model. The findings reveal that AI has become an important part of students’ academic activities due to its perceived usefulness and ease of use. Most participants demonstrated critical thinking behaviors by verifying AI-generated information, comparing multiple academic sources, and evaluating the accuracy of responses before using them for academic purposes. Nevertheless, excessive dependence on AI was found to potentially reduce students’ independent learning and analytical engagement. The study concludes that AI has a dual impact on critical thinking: it can enhance critical thinking when used reflectively and responsibly, but it may also weaken cognitive involvement when relied upon excessively. Therefore, strengthening AI literacy, digital literacy, and critical thinking skills is essential in higher education learning practices.

This study examines the growing debate surrounding the possibility of granting legal personhood to artificial intelligence (AI) systems within contemporary legal frameworks. As AI technologies become increasingly autonomous in decision-making, innovation, and economic interactions, questions arise concerning accountability, liability, and ethical governance. Using a qualitative library research approach, this study analyzes scholarly literature, legal theories, and global regulatory developments related to AI personhood. The discussion explores historical precedents of non-human legal entities, including corporations and natural objects, and compares them with emerging proposals for electronic legal personality. The findings reveal that while functional legal recognition may offer practical solutions for liability and compensation, current AI systems lack consciousness, moral agency, and intentionality required for full legal personhood. Consequently, most jurisdictions prefer human-centered regulatory models emphasizing transparency, oversight, and institutional accountability. The study concludes that extending legal personhood to AI remains premature and that adaptive governance frameworks are more appropriate for managing future AI-related legal challenges

The rapid development of Generative Artificial Intelligence (AI) technology has brought about significant changes in various fields, including higher education. One of its primary functions is to assist students in completing academic writing assignments. This study aims to explore the use of Generative AI in enhancing students’ academic writing skills and to identify the advantages and challenges faced in the field of education. The method used in this study is a literature review, analyzing various scientific works from both domestic and international sources regarding Generative AI, scientific writing, and higher education published between 2023 and 2025. The findings indicate that Generative AI can assist students in formulating ideas, structuring their writing, improving grammar, and boosting writing productivity. Additionally, this technology contributes to enhancing students’ confidence and efficiency in the academic writing process. However, various challenges were also identified, such as the risk of academic misconduct, excessive reliance on AI-generated content, and concerns regarding the accuracy and authenticity of information. Therefore, the use of generative AI must be accompanied by digital literacy and an understanding of ethical considerations to ensure its responsible use in educational settings. This study indicates that generative AI has great potential to improve academic writing skills when used appropriately and responsibly.

The advancement of technology and information, which coincides with digitalization alongside the development of Artificial Intelligence (AI), has colored human social life around the world. Currently, cadets of the Naval Academy often use gadgets/digital tools to access information or other media. With this, there are emerging threats that are very dangerous for individuals and the state. An currently prevalent case is Cyber Crime Identity Theft. Cyber crime identity theft refers to the crime of stealing someone's personal information online for the purpose of fraud or financial gain by stealing personal data such as names, identity numbers, and financial information, to carry out illegal transactions or obtain illegal benefits, which can lead to financial loss and damage the victim's reputation. Indonesia still has a very high number of cases of cyber crime identity theft, but it should be preventable by fostering. the cyber security awareness of cadets at the Naval Academy of the Indonesian National Armed Forces. The conclusion of this research is that by strengthening modern technological infrastructure, both equipment and administrative services, developing modern software and hardware with strong security systems, and tightening regulations regarding access to users' personal data in cyber services, supported by strengthening cyber security institutions, it can enhance the cyber security awareness of cadets at the Naval Academy of the Indonesian National Armed Forces.

Background:The digital transformation in education requires teachers to possess adequate competencies in utilizing technology to support innovative, interactive, and student-centered learning. However, the integration of digital platforms into classroom practices remains limited due to teachers’ varying levels of digital competence and experience. This community service program aimed to improve teachers’ competencies in utilizing digital platforms to support innovative learning implementation at SMAN 8 Banda Aceh. Methods:The program employed a participatory approach consisting of needs assessment, workshops, hands-on practice, mentoring, evaluation, and reflection activities. A total of 40 teachers from various subject areas participated in the program. The training materials included the use of Canva for developing digital learning media, Quizizz for gamified assessments, Google Workspace for collaborative learning, and Artificial Intelligence (AI) technologies to assist in instructional material development and creative learning activities. Program evaluation was conducted through participant observation, assessment of digital learning products, satisfaction questionnaires, and reflective discussions. Results: The results showed that participants successfully produced 18 interactive presentations using Canva, 10 digital modules, 8 Quizizz-based quizzes, and 4 evaluation instruments using Google Forms. Furthermore, teachers demonstrated improved competencies in designing digital learning media, developing technology-based assessments, integrating digital platforms into instructional practices, and utilizing AI as a learning support tool. The high level of participation and enthusiasm among participants indicated the effectiveness of the program in strengthening teachers’ digital literacy and technological competencies. Conclusion: In conclusion, the community service program successfully enhanced teachers’ competencies in utilizing digital platforms to support innovative learning in schools.

The rapid advancement of Internet of Things (IoT) technologies has transformed conventional residential environments into intelligent smart housing systems capable of enhancing energy efficiency, security, comfort, healthcare services, and environmental sustainability. However, existing studies remain fragmented, focusing on specific technologies or applications rather than providing a comprehensive understanding of the smart housing ecosystem. Therefore, this study aims to systematically review the major technologies employed in IoT-based smart housing systems, examine their contributions to residential benefits, and identify the challenges and future opportunities associated with their implementation. This research adopted a Systematic Literature Review (SLR) approach using the Scopus database as the primary source of literature. The review process followed a structured screening and selection procedure, resulting in the inclusion of 26 studies that met the predefined eligibility criteria. The findings indicate that Artificial Intelligence, Blockchain, Edge Computing, Fog Computing, Cloud Computing, Digital Twin, Smart Grid technologies, renewable energy systems, and advanced sensor infrastructures constitute the primary technological foundations of modern smart housing systems. The reviewed studies indicate that these technologies contribute to energy optimization, cybersecurity enhancement, intelligent automation, healthcare monitoring, safety improvement, and sustainable resource management. However, challenges related to security, privacy, interoperability, scalability, computational complexity, and implementation costs remain important barriers to widespread adoption. In conclusion, IoT-based smart housing systems have evolved into integrated and intelligent residential ecosystems, and future development is expected to be driven by the convergence of Artificial Intelligence, distributed computing, Digital Twin technologies, and sustainable smart infrastructure solutions.  

This study explores the role of artificial intelligence in strengthening cybersecurity threat detection frameworks for next-generation network environments. The rapid expansion of cloud computing, Internet of Things ecosystems, and distributed digital infrastructures has significantly increased cybersecurity risks and operational vulnerabilities. Traditional cybersecurity systems often struggle to detect sophisticated and evolving threats due to their dependence on static detection mechanisms. Using a qualitative research approach and content analysis method, this study examines recent developments in artificial intelligence, machine learning algorithms, and intelligent cybersecurity frameworks. The findings indicate that AI-driven cybersecurity systems improve real-time threat detection, anomaly identification, automated monitoring, and predictive security analysis. Machine learning technologies such as Random Forest, Support Vector Machine, and deep learning models demonstrate strong potential for enhancing intrusion detection accuracy and reducing false positive rates. The study also identifies critical challenges related to ethical governance, privacy protection, computational complexity, and adversarial attacks in AI-based cybersecurity systems

This study examines the transformation of business education curricula in response to the rapid expansion of the digital economy. The increasing integration of artificial intelligence, big data analytics, cloud computing, and digital platforms has significantly reshaped industrial demands and professional competencies. Using a qualitative research approach and content analysis, this study explores the challenges and strategic opportunities associated with curriculum transformation in higher education institutions. The findings reveal that universities face critical challenges related to technological infrastructure, faculty readiness, curriculum relevance, and industry alignment. However, digital transformation also provides opportunities for interdisciplinary learning, experiential education, global collaboration, and technology-driven innovation. The study emphasizes the importance of adaptive leadership, faculty development, industry partnerships, and student-centered pedagogies in supporting sustainable curriculum reform. Ultimately, transforming business education curricula is essential for preparing graduates with digital competencies, critical thinking abilities, and innovative skills necessary to compete effectively in the global digital economy.

This study investigates the integration of digital twin technology and machine learning for predictive analysis in smart mechanical systems. The research emphasizes the role of intelligent computational frameworks in improving industrial monitoring, predictive maintenance, and operational efficiency within Industry 4.0 environments. A qualitative content analysis approach was employed by reviewing scientific literature, industrial reports, and previous studies related to digital twins, artificial intelligence, and predictive analytics. The findings indicate that digital twin architectures supported by machine learning algorithms can significantly enhance real-time monitoring, fault prediction accuracy, and maintenance optimization. The integration of IoT devices, cloud computing, and intelligent analytics also improves industrial sustainability, reduces operational downtime, and supports data-driven decision-making processes. Furthermore, the study identifies several technological challenges, including cybersecurity risks, data integration complexity, and computational limitations. Overall, the proposed intelligent digital twin framework provides a promising approach for future industrial innovation and sustainable smart mechanical system management

Political disinformation has become one of the most critical challenges in contemporary digital democracies due to the rapid expansion of social media ecosystems. This study investigates the effectiveness of machine learning approaches in detecting political disinformation across online platforms such as Twitter, Facebook, and political discussion forums. Using a qualitative research design with a content analysis approach, the study examines linguistic manipulation, emotional narratives, sentiment polarity, and behavioral communication patterns embedded in misleading political content. The findings indicate that deep learning models, particularly Long Short-Term Memory (LSTM) architectures, demonstrate superior performance in identifying contextual and semantic inconsistencies compared to traditional machine learning algorithms. The study also reveals that algorithmic amplification, echo chambers, and coordinated bot activities significantly contribute to the rapid spread of political misinformation. Furthermore, the research highlights the importance of ethical artificial intelligence governance, transparency, and digital literacy in strengthening democratic resilience and protecting information integrity within digital communication environments

The quality of boiler feedwater is a crucial factor in maintaining the operational reliability of ship machinery systems against the risks of corrosion and scale formation. This research focuses on the design and development of a real-time monitoring system based on the Internet of Things (IoT) to automatically determine water quality. The hardware development utilizes a NodeMCU ESP32 microcontroller that integrates four main sensors: a pH sensor, a DS18B20 temperature sensor, a Total Dissolved Solids (TDS) sensor, and a turbidity sensor. The methodology applied is an experimental systems engineering model, encompassing circuit design, software programming, and the implementation of simple artificial intelligence. The automatic decision-making process in this tool is designed using zero-order Sugeno fuzzy logic with input parameters of turbidity, salinity, and acidity level (pH). The obtained data is transmitted wirelessly to a Firebase database and visualized through a 0.96-inch OLED display. Functional tests on seven fluid characteristics including pure water, hard water, and muddy water show that this prototype can accurately classify water quality into Good, Fair, and Poor categories. The main contribution of this design is the provision of a preventive, efficient, and applicable boiler maintenance solution for the modern shipping industry.

The development of Artificial Intelligence (AI), particularly Large Language Models such as ChatGPT, has changed the paradigm of creative writing learning in secondary schools. However, empirical evidence regarding the relationship between the intensity of this technology utilization and the quality of students' literary works is still limited. This study aims to examine the relationship between AI utilization and the poetry writing skills of grade XI students at Karya Budi Putussibau Senior High School. The method used was a quantitative correlational method with a sample of N = 27 students selected through a random sampling technique. Data were collected using an AI utilization questionnaire (30 valid and reliable statements, (α= 0.975) and a poetry writing test assessed with an analytical rubric. The results of descriptive statistical analysis showed that the level of AI utilization was in the high category (M =    3.70) and students' poetry writing skills were in the good category (M =  78.41). The Pearson Product Moment correlation test showed a very strong and significant positive relationship between the two variables (r = 0.978; p < 0.01). These findings confirm that proper AI integration acts as a creative catalyst that helps students explore diction and imagery structure in the poetry writing process.

This article examines the impact of dehumanization in the era of Artificial Intelligence (AI) on the formation of the personality of future priests in light of the teachings of Pastores Dabo Vobis, particular number 43-44, as well as from Philosophical and theological perspectives. The development of AI brings significant changes in the way human think, relate, and build their self-identity. On the one hand, AI offers great opportunities to support intellectual formation through access to information, pastoral simulations, and digital learning. However, on the other hand, the dominance of technology has the potential to cause dehumanization, namely a reduction in the quality of interpersonal relationships, the depth of reflection and sensitivity. This study employs a literature review method, examining relevant theological, philosophical, and scientific literature. The findings indicate that excessive reliance on AI can disrupt the development of the human dimension in seminarians, particularly regarding affective maturity, relational competence, and the integrity of personal identity. From a philosophical perspective, humans are understood as free, rational, and relational beings; thus, reducing humans to mere components of an algorithmic system contradicts their very nature. Meanwhile, theology affirms human dignity as the Imago Dei, which cannot be replaced by technology. Therefore, a critical and prudent formation approach is required, one that positions AI as an aid, not a substitute for human relationships.