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This research presents the development of a Decision Support System aimed at assisting in the enhancement of palm oil production using the Analytic Hierarchy Process (AHP) method. The rapid growth in the palm oil industry necessitates a scientific approach to selecting seedlings with optimal growth potential and yield. In this context, the application of artificial intelligence-based technology, such as decision support systems, is essential in the process of selecting superior palm oil seedlings. The objective of this study is to model the criteria for selecting the most superior palm oil seedlings using the Analytical Hierarchy Process (AHP) method by comparing three criteria: leaflet shape, seedling root shape, and prospective trunk size. The result of this process is a decision support system that displays the value range of each palm oil seedling variety, thus facilitating farmers in choosing the most superior seedlings.

The rapid advancements in automation and artificial intelligence (AI) have significantly transformed various industries in Indonesia. While these technologies enhance efficiency and productivity, they also pose severe challenges to employment, notably the increasing prevalence of layoffs (PHK). This article critically analyzes the legal framework in Indonesia governing worker protection against layoffs caused by automation and AI. It highlights the inadequacies in current regulations, such as the Employment Law and the Job Creation Law, in addressing the unique challenges posed by digital transformation. Using a normative legal approach, the study emphasizes the need for adaptive policies, including reskilling, upskilling, and enhanced social security programs like the Employment Loss Insurance (JKP). Lessons from countries like Germany and Singapore underline the importance of government and industry collaboration in workforce readiness. The findings advocate for a holistic and inclusive policy framework to mitigate automation’s adverse effects while leveraging its potential for sustainable development.

The use of artificial intelligence (AI) in banking has grown rapidly in recent years, providing opportunities and challenges in dealing with it. AI in the banking world is expected to facilitate use, strengthen customer data security, and optimize risk management. This article aims to analyze the level of customer user experience towards banking services by utilizing artificial intelligence (AI). The analysis technique used in this study is descriptive with a quantitative approach, involving 100 respondents of various ages, various jobs and various educational backgrounds. The main requirement for respondents is that they are customers of various conventional banks in Ponorogo Regency who have used artificial intelligence (AI)-based banking services. Data were collected through questionnaires supplemented with secondary data in the form of documents related to the implementation of artificial intelligence in the banking service system. The application of AI in the banking sector is the detection and prevention of fraudulent transactions in real time, identity verification through biometrics, identity verification through biometrics, chatbots and virtual assistants for customer service, detecting suspicious activity, and the use of smart wallets. The findings in this study explain that artificial intelligence (AI) is able to increase the efficiency of services to customers, provide a better experience, and improve the quality of banking services. Customers are greatly helped by the use of artificial intelligence (AI)

The development of artificial intelligence deepfake technology has opened up new opportunities in various fields to help speed up human work. However, on the other hand, this technology can also be misused to commit crimes. This research is a Normative Juridical research with a statutory approach and a conceptual approach, and examines the sources of legal materials according to the main problem, and uses prescriptive analysis techniques.The results of the study show: (1) The activity of using artificial intelligence deepfake technology that can cause cybercrime occurs due to attacks on the system, namely AI botnet attacks that have been infected by malicious software and Generative Adversarial Network attacks that have artificial neural networks that can produce data that is similar to the original data so that it is used as a means of committing crimes, and (2) Criminal law regulations in Indonesia regarding the misuse of artificial intelligence deepfake technology in committing cybercrime have not been regulated comprehensively, so that currently it is necessary to establish clear legal regulations in order to provide legal protection for every community.

Stress and burnout among healthcare workers represent a global crisis with significant implications for psychological and physical health, job performance, and interpersonal skills. These conditions are linked to anxiety, depression, suicidal ideation, substance use, poor quality of life, digestive disorders, and cardiovascular diseases. Burnout is characterized by emotional fatigue, depersonalization, and reduced personal accomplishment, often caused by chronic workplace stress. Factors such as demographics, fatigue, and resilience influence its development and severity. Traditional stress management interventions, such as counselling and leave, often prove insufficient in addressing these challenges. Recent advancements in Artificial Intelligence (AI) provide innovative tools for stress and burnout management, including mobile applications offering mindfulness, meditation, and self-care resources. AI systems like IBM Watson and Google DeepMind are being tested to enhance accessibility and effectiveness in stress management. Additionally, Stress Inoculation Training (SIT), involving methods such as meditation, yoga, cognitive-behavioural therapy, and biofeedback, has been recognized as a proactive approach to mitigating stress. This review explores the factors contributing to stress and burnout in healthcare workers and evaluates interventions aimed at improving well-being and productivity, emphasizing the potential of AI and SIT in preventing and managing these conditions.

This research investigated the biomechanical variables of movement analysis and its essentialcomponents for handball players at the College of Physical Education and Sports Sciences, Al-MustansiriyaUniversity, during a league match and university qualifiers. The data was analyzed using binary logisticregression, a mathematical model that defines the link between a dependent variable, which takes the valueof one when a goal is scored against the opposing team and zero when no goals are scored, and independentfactors.The bird swarm algorithm will be used in this research. It is one of the artificial intelligencealgorithms that rely on the intelligence of living flocks by monitoring their movements, such as birds, bees,cats, chickens, and many other swarm algorithms.The conclusions we reached from this study are as follows: When using logistic regression, we foundonly four explanatory variables that affect the dependent variable. They are detailed as follows: The twoexplanatory variables (The maximum height of the hip and Flight time until leaving the ball) and thedependent variable (shooting) have an inverse relationship and affect it. The two variables (Knee angle at themoment of thrust and The instantaneous speed of the ball) have a positive relationship with aiming and affectit.When we used the Bird Swarm algorithm, we found that all the explanatory variables included in thestudy had a significant effect on the dependent variable. The variables (Knee angle at the moment of thrust,Rising angle, Flight angle, The instantaneous speed of the ball, and the horizontal distance of theperformance) have a positive relationship, with the dependent variable (shooting). In contrast (The maximumheight of the hip and Flight time until leaving the ball) have an inverse relationship with the dependentvariable.Using the logistic model helps sports coaches and researchers to estimate and predict models,especially when the dependent variable takes values ​​(one or zero). In contrast, we noticed that the resultswere more accurate and objective when using the bird swarm algorithm. It further helps academics, thoseinterested in sports, and coaches benefit from these results.

Computer intelligence, particularly Artificial Intelligence (AI), has become a cornerstone in advancing bioinformatics. This study aims to explore the role of AI in addressing the challenges of analyzing complex biological data, especially in genomics, proteomics, and metabolomics. Using machine learning (ML) and deep learning (DL) algorithms, AI efficiently processes large-scale data, accelerates genomic research, predicts protein structures, and identifies disease biomarkers. However, challenges such as data quality, computational limitations, and privacy issues remain barriers to its implementation. The findings of this study highlight the importance of continuous innovation, multidisciplinary collaboration, and strict regulations in AI applications. In conclusion, AI holds great potential to revolutionize bioinformatics, significantly impacting scientific research and the development of global healthcare systems.

The Society 5.0 era brings significant changes in the world of education, with the integration of technologies such as artificial intelligence and big data. Amidst this progress, challenges such as the dehumanization of learning interactions, inequality in access to technology, and weak implementation of ethical values ​​are issues that must be addressed. This study highlights the existence of professional ethics in teaching as a foundation for dealing with the educational crisis in the Society 5.0 era. Barriers such as lack of understanding of technology and administrative pressure are identified, along with strategies to strengthen professional ethics, such as continuous training and integration of moral values ​​in learning. As a result, this study emphasizes the importance of the role of teachers in creating technology-based education that remains humanistic and ethical.

In the digital era of the 21st century, technology continues to develop rapidly, influencing various aspects of life, including business operations. This journal explores the concept of Technology 21 and its application in the development of integrated information systems for modern business. This research aims to understand how technological innovations, such as artificial intelligence, Internet of Things (IoT), and big data, can be integrated in information systems to increase operational efficiency and effectiveness. Through analysis of various case studies, this journal shows that integrated information systems utilizing Technology 21 not only facilitate better decision making, but also improve collaboration between departments and provide competitive advantages. By presenting the main components of an integrated information system, such as hardware, software, data, procedures, and user roles, this journal provides a comprehensive overview of the challenges and opportunities faced by businesses in adopting the latest technology. It is hoped that the findings of this research can become a reference for practitioners and researchers in understanding the importance of technology integration in information management in the modern era.  

In recent decades, artificial intelligence (AI) has significantly advanced and shown great potential across various fields, including bioinformatics. This paper examines current trends in AI applications within bioinformatics, highlighting future potentials and the challenges of integrating these technologies. The research utilizes secondary data collection from scientific literature, books, conference reports, and official documents on AI and bioinformatics, sourced from reputable databases like Scopus, IEEE, PubMed, and Google Scholar. Through comparative analysis, similarities, differences, and technological advancements were identified and discussed. Descriptive narrative interpretation was employed to provide a holistic view of AI trends and potential in bioinformatics. Key findings indicate that AI, particularly machine learning and deep learning, is instrumental in genomic data analysis, protein structure prediction, drug discovery, and clinical bioinformatics. Furthermore, the study underscores the benefits of AI in enhancing data analysis accuracy and efficiency, while addressing ethical and technical challenges. Future prospects emphasize the importance of interdisciplinary collaboration to fully leverage AI's capabilities in bioinformatics.

This research investigates the role of context in pragmatic interpretation, focusing on how context influences the understanding of meaning in communication. By examining various aspects such as implicature, deixis, presuppositions, and speech acts, the study highlights how speakers and listeners navigate meaning through contextual clues. The research employs a qualitative approach, gathering data from real-life interactions, including interviews and observations. Findings reveal that context is not just a passive backdrop but an active participant in shaping meaning, particularly in intercultural communication. This study emphasizes the dynamic nature of context and its indispensable role in pragmatic interpretation, offering insights for language teaching, intercultural communication, and artificial intelligence applications.

Artificial intelligence (AI) is now an integral part of the digital transformation of various industries including the maritime sector. In the context of maritime law, AI has the potential to optimize various aspects ranging from maritime traffic management, Accident risk mitigation to monitoring international environmental maintenance. However, the application of AI also presents a variety of very complex challenges. This research highlights the legal, regulatory and ethical challenges that arise along with the application of AI in the maritime sector. Among them are the lack of clarity regarding legal responsibility for incidents involving autonomous ships, the lack of international standards governing the use of AI in operations maritime, as well as threats to data privacy and security from the use of increasingly sophisticated technology. Apart from these challenges, This research also discusses innovations that are already developing, such as the development of new legal frameworks for maritime AI, blockchain integration technology in the supply cycle chain as well as cross-border collaboration to developing regulatory standards that are cohesive and responsive to technological developments, this research concludes that to ensure the safe and responsible implementation of AI in the maritime sector, a multidisciplinary approach involving law, technology and international cooperation is needed, AI can be the main catalyst in creating a safer, more efficient and sustainable maritime ecosystem in the future.

This study aims to develop an automated pipeline for data cleaning using Pandas and Scikit-learn. The data cleaning process is often performed manually, requiring a long time and prone to errors. This study uses a quantitative experimental method with a dataset of 100,000 rows of e-commerce transaction data. The results show that the automated pipeline reduces missing values by 95.7% and outliers by 91.7%, and accelerates processing time by 35% compared to manual methods. The distribution of data after cleaning becomes more stable, allowing for more accurate analysis. This study contributes to the development of a more efficient and accurate automated data cleaning approach.Keywords: Systematic Literature Review, Artificial Intelligence and Marketing Strategy.

The rapid advancement of technology has made artificial intelligence (AI), particularly generative chatbots, an integral part of everyday life. These chatbots, powered by Natural Language Processing (NLP) and deep learning technologies, are widely used in various fields such as customer service, education, and entertainment. However, the increasing prevalence of such technologies brings forth important philosophical concerns, particularly in the realm of axiology—the branch of philosophy that deals with the nature of values, including the practical and ethical implications of knowledge and technology. This study investigates the practical benefits and ethical responsibilities associated with generative chatbots, using ChatGPT as a case study. The research examines whether ChatGPT adheres to the axiological principles of science, specifically its usefulness in enhancing human life and its ethical responsibilities. Through a qualitative content analysis, this research evaluates the responses of ChatGPT to a series of questions based on the axiological framework outlined by Sumantri. The study focuses on two main aspects of science's axiological evaluation: the practical benefits of science and technology, and the ethical responsibilities tied to their application. The findings indicate that ChatGPT is capable of providing useful insights that contribute to human understanding, improve quality of life, simplify complex tasks, and offer solutions to various problems. However, the ethical considerations of AI technology, such as fairness, transparency, and accountability, remain a crucial area of concern. This research highlights the importance of balancing technological progress with ethical responsibility, emphasizing that AI systems like ChatGPT must be developed and applied in ways that align with human values to ensure their positive impact on society.

Bioinformatics is an interdisciplinary field that combines biology, computer science and statistics to analyze biological data and translate it into effective therapies. With technological advances, such as next-generation genetic sequencing, bioinformatics enables the development of personalized therapies based on an individual's genetic profile. This approach provides more effective treatment and reduces the risk of side effects. In addition, the integration of artificial intelligence (AI) accelerates big data analysis, predicts therapy response and identifies disease biomarkers. Despite challenges such as the need for big data infrastructure and ethical privacy issues, the outlook for bioinformatics is bright. Through global collaboration and a multi-omics approach, bioinformatics is expected to become a key foundation in future medical therapy innovation.

Collaborative innovation has become crucial for organizations that wish to stay relevant and competitive in the quickly evolving digital age. There are plenty of opportunities, but there are also several obstacles to overcome. Information and data security in a digital world vulnerable to cyberattacks is one of the biggest problems. Sophisticated security measures must be implemented by organizations to safeguard confidential data that is shared among team members. Resistance to change is also an obstacle that needs to be overcome. When organizations adopt new technology, employees often face difficulties adapting. Organisations can create more efficient and effective solutions with the latest technologies, such as artificial intelligence and data analysis. Collaboration across industries and disciplines enables a holistic approach to solving complex problems. By understanding and overcoming existing challenges, organizations can harness the potential of collaborative innovation in the digital era to create added value, increase efficiency, and ensure business continuity in an ever-changing world.

This research aims to measure the influence of dependence on the use of artificial intelligence (AI) applications on the learning effectiveness of Islamic Education Management students. Using quantitative research methods, data was collected through questionnaires distributed to 150 students at several universities. Data analysis was carried out using a simple linear regression test to determine the correlation between the dependency variable on AI applications (X) and learning effectiveness (Y). The research results show that 78% of respondents use AI applications in their daily learning activities, with 65% of them feeling more efficient in accessing information. However, there are 40% of students who show decreased motivation for independent learning due to dependence on AI applications. The results of the regression test show that there is a significant positive correlation between the use of AI applications and learning effectiveness with a correlation coefficient of 0.52 and a significance of p < 0.05. These findings indicate that the use of AI plays an important role in increasing learning effectiveness, but also has the potential to reduce motivation for independent learning. It is hoped that the use of AI will be accompanied by learning strategies that support student independence and critical thinking.

Excessive sugar consumption has become a serious public health problem. Increasing patterns of food and drink consumption in line with changes in modern lifestyles have contributed to an increase in the prevalence of non-communicable diseases such as obesity, type 2 diabetes and cardiovascular disorders. This study analyzes and analyzes the use of Artificial Intelligence (AI), especially Deep Learning techniques and Neural Network algorithms, in the classification of sugar content in sweetened drinks. The Systematic Literature Review (SLR) method was used to filter relevant studies published between 2020-2024. The study results show that AI is able to provide more efficient and accurate solutions than manual methods. However, although the literature results show great potential, the application of AI in sugar content classification still requires further empirical research. This study emphasizes the importance of developing AI models tailored to the characteristics of sweetened drinks to support consumer decision making regarding healthier drink choices.

Research on Artificial Intelligence (AI) in finance has been growing significantly alongside its increasing implementation in the financial sector. This development raises questions about the specific financial areas and AI technology applications that are most frequently explored as research topics within AI in finance. This study aims to address these questions by employing a systematic literature review (SLR) method, analyzing journal articles indexed in Scopus (Q1–Q4) and published between 2020 and 2024. A search conducted using Publish or Perish on the Scopus database identified 496 records, which were subsequently filtered to 94 articles using the PRISMA protocol. The selected articles were examined through bibliometric analysis using VOSviewer, followed by content analysis. The findings reveal that fintech and risk management are the most frequently discussed financial areas in AI in finance research. Moreover, machine learning emerges as the most commonly addressed AI technology application in this domain. Notably, the combination of machine learning and risk management stands out as the most prominent research topic.    

Artificial Intelligence in STEM education has gained attention because it has the potential to transform the learning and teaching process. The study aims to systematically analyze the application of AI technology in STEM education, focusing on the impact on student engagement, learning outcomes, and teaching methods. The study used the Systematic Literature Review (SLR) method with PRISMA guidance, Articles published in the last ten years, to ensure the selection process on the latest trends. The main findings show that AI-based tools, such as adaptive learning systems, intelligent tutoring systems, and automated assessment platforms, significantly enhance the personalized learning experience and encourage students' critical thinking skills in STEM learning. Challenges such as ethical considerations, data privacy issues, and the digital divide are still issues that need attention. Overall, the study shows that AI has a crucial role in STEM education in the future and recommends a strategic framework to overcome the challenges of AI implementation. The main findings of the article provide a basis for educators, policymakers, and researchers to develop and implement AI-based innovations effectively in STEM education.