Publication Search

The function of artificial intelligence (AI) in creating a positive consumer experience on the Shopee e-commerce platform is covered in this study. Nowadays, artificial intelligence (AI) plays a significant role in the e-commerce sector, allowing businesses to offer more individualized, quick, and safe services. This study examines how Shopee customer satisfaction and loyalty are impacted by the use of AI technologies, including chatbots, tailored product suggestions, fraud detection, and inventory management. This study found that AI plays a significant role in making shopping more responsive and convenient by analyzing data on the use of chatbot services, the efficacy of personalized recommendations, the accuracy of predicting customer needs, transaction security, and the efficiency of logistics management. According to the study's findings, implementing AI technology reduced service response times by up to 20% and increased customer satisfaction by 15%. In addition to enhancing service quality, the use of AI increases Shopee customer loyalty. These results demonstrate how AI in e-commerce holds enormous promise for fostering startup expansion and enhancing client connections.

Artificial Intelligence (AI) has increasingly become an integral component of modern education, offering significant opportunities to improve accessibility, efficiency, and personalized learning. This study explores students’ perceptions of AI in educational contexts, focusing not only on its practical benefits but also on ethical concerns, particularly regarding data privacy and social responsibility. Adopting a mixed-method design, the research collected data through surveys and classroom observations. The survey responses were analyzed using descriptive statistics to identify trends in perceptions, while observation notes were thematically coded to capture students’ real interactions with AI applications. The findings reveal that students generally exhibit a positive attitude toward AI, recognizing its potential to enhance learning experiences by making education more accessible and efficient. However, the study also highlights that concerns over data privacy and the potential misuse of information remain central issues. Moreover, a notable gap exists between students’ appreciation of the practical benefits of AI and their awareness of ethical dimensions such as algorithmic bias and digital responsibility. This imbalance suggests that while students are ready to embrace AI as a tool, they may not yet be adequately prepared to address the broader ethical implications of its use. The discussion situates these findings within the framework of digital ethics, emphasizing the need for education systems to integrate ethical literacy alongside technological skills. By doing so, schools and universities can ensure that students are both technologically competent and socially responsible. The study concludes that although AI is welcomed in educational settings, greater emphasis on digital ethics education is essential to maximize its benefits and minimize potential risks. This research provides a basis for future studies on policy development and ethical practices for AI in the classroom.

Energy efficiency has become a critical focus in manufacturing plants due to rising operational costs and increasing environmental concerns. The growing importance of energy management is driven by the need to reduce energy consumption, lower emissions, and enhance overall operational efficiency. Traditional maintenance practices, such as reactive and preventive maintenance, often lead to unnecessary downtime, high repair costs, and inefficient energy usage. In contrast, predictive maintenance (PdM), supported by Internet of Things (IoT)-enabled sensor networks, offers a proactive approach to minimizing energy waste by predicting equipment failures before they occur. This study develops a predictive maintenance framework using IoT-based sensor networks to optimize energy usage and reduce energy losses in manufacturing plants. The research begins with an overview of IoT sensor network architectures and their applications in industrial automation, including sensors such as temperature, vibration, and pressure sensors. It explores predictive analytics techniques, such as machine learning and artificial intelligence, used for failure prediction, which are key to enhancing energy efficiency. The study emphasizes how predictive maintenance contributes to industrial sustainability by reducing carbon footprints and optimizing energy consumption. The research methodology involves the installation of IoT sensors in critical machinery, real-time data analysis using machine learning algorithms for failure prediction, and energy consumption measurement before and after implementing IoT-based interventions. The results show significant improvements in energy consumption efficiency and operational productivity. Predictive maintenance led to reduced unplanned downtime, increased equipment reliability, and a more sustainable manufacturing process. However, challenges such as sensor integration, initial setup costs, and data security concerns were identified. The study concludes with recommendations for integrating IoT-based predictive maintenance systems into manufacturing plants to further optimize energy usage and promote sustainability.

Researchers argues that large language models are critical to the advancement of artificial intelligence and will play a vital role in future progress. Despite criticism and occasional bans, these models are persistent and poised to continue. This analysis delves into the potential benefits and challenges of utilising extensive language models in education, considering perspectives from both students and educators. These models' current status and applications are briefly reviewed, emphasising their use in generating educational material, increasing student engagement, and personalising learning experiences. The discussed challenges include the need for educators and students to develop skills and literacies to understand and navigate the technology and its limitations. Employing a strategic and pedagogical approach that stresses critical thinking and fact-checking is a crucial component of effectively integrating these models into educational institutions. AI applications in education often encounter additional challenges, including potential biases, the necessity for ongoing human oversight, and the risks of misuse. However, these challenges may present educational opportunities for students to become familiar with social biases, complexities, and risks associated with AI. The essay presents solutions for effectively addressing these challenges to ensure the responsible and ethical use of large language models in education.

The Industrial Revolution 4.0 era has brought significant changes in various aspects of life, including social relations in society. Digital technologies such as social media, the internet of things (IoT), and artificial intelligence (AI) have become an integral part of human interaction. This article aims to analyze the influence of the use and implementation of digital technology on the pattern of social relations in society. This study uses a qualitative method with an in-depth literature approach. The results of the analysis show that although digital technology provides convenience in communication, there are negative impacts such as decreased face-to-face interaction and increased social isolation.

The use of AI is seen as aiding the learning process, and students have used AI for various purposes in their coursework. This article aims to investigate the impact of AI use on their innovation and creativity, as well as the possibility of AI replacing lecturers. A qualitative study was conducted with 53 student respondents. Questions were asked about the definition of AI, the applications of AI they use, whether AI can enhance creativity and innovation, and whether AI will one day replace lecturers. The findings indicate that AI is artificial intelligence that serves as an aid in the student learning process. The majority of students use ChatGPT, and 37 students stated that AI can enhance their innovation and creativity by providing inspiration and initial ideas. AI is also a tool that accelerates the learning process and facilitates creative work, as well as AI as a means of developing skills such as critical reasoning and data analysis. However, the majority also stated that dependence on AI can actually reduce critical thinking and creativity. Regarding the role of lecturers, 41 students stated that AI cannot replace them. The implication of these findings is that AI can be used wisely while still being aware of its limitations and ethical use. The role of lecturers must be further enhanced to ensure that student learning processes address AI's limitations and allow students to further enhance their innovation and creativity, while simultaneously mitigating the associated risks.

The legal framework has significant potential to address cybercrime with the help of Artificial Intelligence (AI), which increases the efficiency of detecting, investigating and prosecuting increasingly sophisticated cybercriminals. This technology can perform big data analysis, pattern recognition and identification of suspicious behavior, but the legal framework needs to be updated to cover new crimes such as AI-based fraud and automated cyberattacks. The challenges in law enforcement related to the misuse of AI are quite complex, especially due to the lack of specific regulations to regulate its use in the context of cybercrime. Existing regulations often do not cover new situations, thus reducing the effectiveness of law enforcement. To overcome these challenges, legislators need to update regulations and develop ethical guidelines, while international collaboration and capacity building of law enforcement through education and training are also essential to increase the effectiveness of handling cybercrime.

The presence of Artificial Intelligence (AI) has a significant impact on the preparation of financial reports, especially in terms of efficiency and accuracy. This article aims to examine the influence of AI on the process of preparing financial reports that were previously carried out conventionally and the challenges faced by companies in adopting it. This study uses a literature study method to identify trends and findings related to the use of AI in accounting. The results show that AI is able to improve operational efficiency, data analysis, and security, although there are challenges in its implementation, such as human resource adaptation and data security.

Curriculum administration is all business processes that have been planned and attempted deliberately and earnestly as well as continuous guidance on teaching and learning activities effectively and efficiently in order to achieve the educational goals that have been set. The importance of curriculum administration in educational institutions, which includes the selection of materials, teaching methods, and assessments that are in accordance with educational goals and student needs. Curriculum administration plays a role in ensuring that the curriculum is well structured and organized through various levels of planning, implementation, and supervision. Curriculum evaluation is also an important part of education management, aiming to collect information about the effectiveness of curriculum implementation and the impact of learning outcomes. In addition, this journal highlights the role of teachers in curriculum administration as implementers, adapters, developers, and researchers. The presence of artificial intelligence (AI) technology presents an opportunity for educators to carry out a learning process that focuses on the needs, interests, and learning styles of students, because the independent curriculum requires educators to carry out differentiated learning as an initiative in facilitating students, especially in Pancasila education, which incidentally is still carried out by conventional models or methods.

The era of technological disruption has brought about significant changes across various sectors, including the legal sector. One of the most prominent developments is the use of artificial intelligence (AI) in the judicial system. AI can be utilized for a wide range of tasks, from analyzing legal documents to predicting case outcomes, and even acting as a legal assistant. This study employed a qualitative approach by analyzing various sources such as journals, documents, and relevant research findings. The results of the study indicate that while AI offers numerous benefits, its application in the judiciary also faces several challenges. One of the primary challenges is the issue of data bias. The performance of AI heavily relies on the quality of the data used to train it. If the data contains biases, the resulting AI will also be biased. Additionally, concerns about privacy and data security are significant issues that need to be addressed.

This research explores the transformative impact of digitalization on maritime operations, focusing on emerging technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), and blockchain. Through qualitative research and descriptive analysis, perspectives from maritime professionals, lecturers, and graduates were examined to understand the integration of these technologies in maritime management, operations, and education. The findings reveal that digital tools significantly enhance operational efficiency, decision-making, and sustainability while highlighting challenges in workforce training and the gap between theoretical knowledge and practical application in maritime education. Despite the benefits, full optimization of these technologies is still developing due to varied adoption levels across the industry. The research emphasizes the need for investment in infrastructure, continuous professional development, and educational reform to fully leverage digitalization in the maritime sector.

The process in artificial intelligence is similar to the process that occurs in the human brain. The pattern recognition process is to determine whether the input and output values ​​are the same so that the system can detect the pattern. A reciprocal relationship between the input and output levels is made possible by the two interconnected layers of the Bidirectional Associative Memory (BAM) technique. As a result, this technique can serve as an associative memory, meaning that some of the data stored in it can be called upon.. The load signal is transmitted from the input layer X to the output layer Y in this procedure. The weight value will be modified by computing the outcomes between the ranks [1,0]. In this work, the active sigmoid function is employed. Three input characters—the letters S, O, and X—are used in this study's 5x5 order matrix. A pattern [27 4] that matches the target is produced by the target letter S [1,-1], a pattern [27 -3] that does not match the target is produced by the target letter O [1.1], and a pattern [-37 21] that matches the target is produced by the target letter X [-1,1]. Consequently, not all patterns are able to meet the established objectives.

In the digital era, technology plays an important role in designing learning that is more interactive, personalised and adaptive. This research examines the role of learning technology in learning design through a qualitative approach based on a literature review. The use of technology, such as Learning Management Systems (LMS), virtual reality (VR), and artificial intelligence (AI), shows potential to increase student engagement as well as provide a more flexible learning experience. Blended learning approaches are also proven to increase engagement and provide flexibility in the learning process. In addition, technology supports competency-based learning that enables real-time evaluation of student progress through learning analytics. However, there are challenges in implementing technology, such as the digital divide, teacher readiness, and infrastructure limitations. Therefore, the successful integration of technology in learning design requires support from educational institutions and policies to provide adequate training for teachers and appropriate technology infrastructure. This study concludes that technology integration in learning design provides significant benefits in improving learning quality, but still requires a systematic and adaptive approach.

This journal focused on the juridical analysis of legal protection for the victims of Artificial Intelligence misuse based on Indonesia’s Law. In this context, AI misuse refers to the use of AI technology that harms an individual or group, physically, mentally, and financially. Firstly, this journal explored how Indonesia’s law protects the victims of AI misuse. Although Indonesia has developed some regulations and policies, the legal protection of the victims of AI misuse still has become a challenge. The existing laws did not cover the specific aspects of AI misuse, including data privacy, algorithm discrimination, and other social impacts. Secondly, this journal discussed the challenges and obstacles in implementing legal protection for the victims of AI misuse in Indonesia. Some vital challenges included the lack of a general understanding of AI, specific laws for AI, and the gaps between the technology advancement and the existing law. Moreover, another challenge came from the lack of an international law framework that regulates the use of AI. This journal suggested the need for regulation improvement, public education about AI, and international cooperation in regulating AI. Besides, further research is needed to understand the bad impact of AI misuse and how the law can be more effective in protecting the victims.

This research explores the impact of using the application on Micro, Small, and Medium Enterprises (MSMEs) in the city of Semarang in the context of digitalization and the implementation of Artificial Intelligence. (AI). Research data were obtained from a survey of 100 MSME actors and in-depth interviews. The analysis method used is qualitative descriptive. The research results indicate that the application can potentially enhance MSMEs' operational and marketing efficiency through a digital system. However, it still faces the challenge of low digital literacy among MSME actors. The discussion shows that this innovation can improve the competitiveness of business actors in the digital era, especially in understanding consumer behavior and logistics management. The research conclusion emphasizes that implementing the application can be a solution to increase MSME income, help them adapt to market changes, and effectively utilize digital technology.

This research investigates students’ perceptions of Artificial Intelligence (AI) as a tool for learning English at MTsN 4 North Aceh. Quantitative descriptive method was employed in this study that involving 40 students as participants. Data were collected through Likert-scale questionnaires and semi-structured interviews to assess students’ attitudes towards AI in relation to motivation, and skill development (listening, speaking, reading, and writing). The results indicate that most students perceive AI positively, appreciating its ability to enhance their understanding of English materials, increase motivation, and provide real-time feedback. Many students highlighted that AI tools offered interactive and flexible learning opportunities. Overall, artificial intelligence (AI) is not seen as a replacement for conventional teaching, even though it is a useful tool for learning English. The best strategy could be to combine AI with human instruction, maintaining a balance between the flexibility and immediate feedback that AI offers and the individualized help that teachers can provide.

The detection of computer network attacks is becoming increasingly important as the complexity and frequency of cyber-attacks threatening information systems and network infrastructure continue to rise. These attacks may lead to severe consequences, including data breaches, service disruptions, and financial losses. To address these challenges, artificial intelligence techniques have become a major focus in the development of more effective, adaptive, and reliable intrusion detection systems. Among various classification algorithms, the C4.5 decision tree has demonstrated strong performance due to its simplicity, interpretability, and high classification accuracy. This study aims to apply the C4.5 algorithm for network attack detection using a comprehensive dataset that includes multiple categories of attacks and normal network activities. The proposed methodology consists of several stages, including data preprocessing, feature selection, decision tree model construction, and performance evaluation using standard metrics such as accuracy, precision, recall, and F1-score. Data preprocessing is performed to handle missing values, normalize data, and reduce noise, thereby improving the overall quality of the dataset and enhancing classification results. The experimental results indicate that the C4.5 decision tree algorithm effectively classifies network traffic into attack and normal categories with a satisfactory level of accuracy. The model successfully identifies attack-related patterns and highlights significant features that influence detection performance. Further analysis reveals that appropriate feature selection and parameter tuning significantly contribute to improving model reliability and robustness. This research provides a valuable contribution to the development of efficient, accurate, and practical network intrusion detection systems. The proposed approach is expected to strengthen information security frameworks and support proactive defense strategies against increasingly sophisticated cyber threats, thereby enhancing the protection of critical network infrastructures.

Collaboration of computer science disciplines with other disciplines has been widely carried out, for example, medical science. Expert systems for disease diagnosis are one of the many computer programs used by doctors to assist them in their work and provide good results. Researchers in the field of artificial intelligence are working to improve existing systems to cover their shortcomings. Expert systems are used in many applications for disease diagnosis. Expert systems have been used in various industries and have had a significant impact. An innovative method for diagnosing diseases is the application of case-based reasoning systems, compared to expert systems. The solution to diagnosing and treating diseases can be found in the expert system for diagnosing pulmonary tuberculosis. Because this web-based system is based on a web application, all pulmonary tuberculosis patients can access it. A rule-based system that applies the CBR method can identify various types of diseases through the use of weighting techniques and offer treatment recommendations.

Digital technology innovation has become the main driver in reforming the education system in the modern era. The use of digital technologies, such as online learning platforms, artificial intelligence, and data-based education management systems, has changed the way education is learned, taught, and managed. These innovations not only increase the accessibility and flexibility of learning, but also enable the personalization of the learning experience for students. This article aims to examine the role of digital technological innovation in driving education system reform, as well as highlighting the opportunities and challenges faced in the transformation process. The results of this research conclude that digital technology innovation has great potential to encourage education system reform, as long as it is accompanied by supportive policies, increasing the competency of teaching staff, and equitable development of technological infrastructure. It is hoped that this reform can create an education system that is more inclusive, adaptive and relevant to future challenges.

This article explores the utilization of Artificial Intelligence (AI) as a tool for enhancing efficiency in public communication within the BANI era (Brittle, Anxious, Nonlinear, Incomprehensible). The aim of this research is to investigate how AI can improve the effectiveness of public communication amidst uncertainty and complexity. The research employs a descriptive qualitative approach with a literature review, analyzing data from relevant journal articles, books, and case studies. The study finds that AI plays a crucial role in addressing system fragility through misinformation detection, reducing public anxiety by providing personalized and responsive information, and managing uncertainty and complexity through predictive analysis and data simplification. The results indicate that AI can enhance the efficiency and clarity of public communication but must be complemented by stringent regulations and ethical considerations to ensure responsible use. With the right approach, AI can be an effective tool in improving public communication in this challenging era.