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Smart cities are increasingly leveraging advanced technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Big Data Analytics to optimize urban management and improve the quality of life for citizens. However, managing vast and diverse datasets from numerous sources in real-time presents several challenges. This research proposes a modular framework that integrates distributed data processing engines with container-based workflow orchestration to address scalability, latency, adaptability, and fault tolerance in smart city data analytics. The framework utilizes cloud native technologies, including Apache Spark and Kubernetes, to efficiently manage resources and ensure high availability. The experimental setup tested the framework’s ability to handle dynamic data loads, demonstrating scalability through real-time resource allocation and low-latency processing. The adaptability of the framework was evident in its seamless integration with various data sources, such as environmental sensors and traffic management systems, which require different processing methods. Additionally, the framework’s modularity provided fault tolerance, enabling continued operation even if individual components failed, a crucial feature for mission-critical applications in smart cities. Compared to traditional monolithic systems, the proposed framework outperformed in flexibility, scalability, and performance, offering significant improvements in handling real-time data streams. Despite these advantages, challenges remain, particularly in integrating heterogeneous data formats and optimizing real-time processing for high-priority applications. The research highlights the importance of scalable data analytics and efficient workflow orchestration for the future of smart city platforms, offering a foundation for the development of more resilient, adaptable, and efficient cloud native infrastructures.

The rapid development of artificial intelligence (AI) in the medical field has become an important part of the learning process and health services. Preparing medical students as future healthcare professionals to understand, use, and implement AI responsibly is a crucial aspect. This level of readiness can vary depending on their knowledge, abilities, perceptions, and ethics in using AI. This study aims to determine the readiness of young medical students in the Surabaya area in using artificial intelligence based on these four domains, and to compare scores between first-year professional students and undergraduate students. This study used a quantitative descriptive design with a cross-sectional approach. The instrument used was the Medical Artificial Intelligence Scale for Medical Students questionnaire, which consists of four domains: knowledge, abilities, perceptions, and ethics. The study sample was first-year and second-year professional students of the Faculty of Medicine, Ciputra University. Data analysis was performed using descriptive statistics including mean values, standard deviations, and frequency distributions for each domain. The results showed that the total readiness scores for DM1 (89.95 ± 11.84) and DM2 (88.38 ± 8.85) showed a positive picture, with minimal mean differences. The knowledge and skills domain showed almost uniform values ​​between the two groups, while the ethics domain had the highest stability with a very small mean difference. These findings indicate that the readiness of professional students at the Faculty of Medicine, Ciputra University, towards the use of AI shows a positive and relatively even picture at all levels.  

The need for learning materials that can adapt to students’ abilities and characteristics has made the development of AI-based adaptive learning resources increasingly important in elementary education. However, limited digital literacy and high workloads among teachers hinder optimal implementation. This Community Service Program aimed to enhance the competencies of teachers at Sekolah Alam Ananda Mandiri Slawi through workshops, guided practice, and hands-on development of AI-supported adaptive materials. The method included preparation, conceptual presentation, technology demonstration, guided practice, pre–post evaluation, and follow-up support. The results showed a significant improvement, with teachers’ competence in the “good” category increasing from 22.5% to 82.5%, and all participants (100%) successfully producing at least one adaptive module and one deep-learning-based material component. Furthermore, 92% of participants reported that the program greatly improved their digital literacy and pedagogical understanding. Thus, this activity effectively strengthened teachers’ competencies and provided a strong foundation for the sustainable integration of AI-based adaptive learning materials in elementary classrooms.

This study aims to determine the effectiveness of the use of the artificial intelligence-based application (AI-Based Application) Duolingo in improving vocabulary mastery of EFL (English as a Foreign Language) students in Indonesia. This study used a descriptive quantitative approach with a single-group pre-test and post-test design involving 20 students of class VII C of SMP Negeri 1 Mappakasunggu. Data were collected through vocabulary tests, questionnaires, and classroom observations. The results showed a significant increase in students' vocabulary mastery, marked by an increase in the average score from 61 (fair category) in the pre-test to 78 (good category) in the post-test. Most students gave a positive perception of the use of Duolingo, especially regarding the gamification features, instant feedback, and simple and attractive display, which can increase motivation and learning engagement. The results of the observation also showed that students were more active and enthusiastic in using this application compared to traditional learning methods. Thus, Duolingo can be said to be effective as an AI-based learning medium to improve vocabulary mastery of junior high school students in Indonesia.  

The gap between academic curriculum content and modern industrial needs is often an obstacle for fresh graduates in the Information Technology field, particularly in the rapidly evolving Artificial Intelligence (AI) sector. This study aims to identify the relationship patterns among technical competencies (hard skills) most demanded by the global industry. The method employed is Association Rule Mining with the Apriori algorithm to discover association rules between skills, and Network Graph Analysis to visualize the topological map of these competencies. The research dataset covers 15,000 AI job vacancies from the 2024-2025 period, analyzed in depth using Support, Confidence, and Lift Ratio evaluation parameters to validate the strength of relationships between items. The results show that Python is the central competency with the highest frequency of occurrence. Strong association rules were found indicating that proficiency in TensorFlow has a high probability of requiring Python proficiency. The Network Graph visualization reveals three main competency clusters: Data Engineering Ecosystem, Deep Learning, and Infrastructure. These findings offer a strategic foundation for aligning curricula with the job market. Focusing on strengthening the identified competency clusters is expected to directly enhance the relevance and work readiness of graduates.

The development of digital technology and the demands of 21st-century learning require teachers to implement meaningful and student-centered learning through an immersive learning approach. However, many elementary school teachers still face limitations in understanding the concept of immersive learning and utilizing technology-based learning media, especially three-dimensional (3D) and Artificial Intelligence (AI)-based media. Therefore, this Field Study activity aims to improve the knowledge and skills of teachers and students in implementing immersive learning through the use of 3D-based learning media and AI technology at the UPTD of SD Negeri 155684 Lubuk Tukko 1, Pandan District. The activity was implemented through a four-day workshop involving teachers and students, using an approach of socialization, demonstrations, hands-on practice, discussion, and reflection. The workshop materials covered the concept of immersive learning, the use of 3D-based learning media, gamification of learning through 3D Media applications, and the use of Artificial Intelligence in developing teaching materials and learning media. Evaluation of the activity was conducted through pre- and post-tests, participant observation, and analysis of the resulting learning products. .The results of the activity showed an increase in teachers' understanding and skills in designing and implementing technology-based immersive learning. Teachers were able to produce interactive learning media, teaching modules, and evaluation questions using 3D media, 3D media, and AI. Furthermore, this activity also increased teachers' motivation, creativity, and awareness of the importance of digital literacy in learning. This Field Study activity made a positive contribution to improving teacher competency and supporting the creation of more innovative, interactive, and relevant learning that reflects the characteristics of 21st-century learners.

The development of Artificial Intelligence (AI) has significantly influenced the formation of contracts in civil law, particularly through the automation of clause drafting, risk analysis, and the standardization of contractual documents. The use of AI in contract drafting raises complex legal issues, especially concerning the validity of agreements and the attribution of legal liability in the event of default. This study aims to analyze the validity of contracts created through Artificial Intelligence from the perspective of Indonesian civil law and to examine models of legal liability in AI-based contracts. This research employs a normative legal method with statutory and conceptual approaches, examining the provisions of the Indonesian Civil Code, particularly Article 1320, as well as legal doctrines and scholarly perspectives on digital contracts and AI. The findings indicate that AI-based contracts are, in principle, legally valid as long as they fulfill the requirements of a valid agreement, namely the consent of the parties, legal capacity, a specific object, and a lawful cause. Artificial Intelligence cannot be positioned as a legal subject because it lacks intent, consciousness, and the capacity to bear rights and obligations, and therefore functions solely as a technological tool. Consequently, legal intent and liability remain attached to the human or legal entity that uses, controls, or benefits from AI. This study also emphasizes that the primary challenge of AI-based contracts lies in the absence of specific legal regulations governing the allocation of liability among AI users, system providers, and developers, particularly when default occurs due to algorithmic errors or system failures. Therefore, clearer, adaptive, and comprehensive regulations are required to ensure legal certainty, protect the parties involved, and maintain a balance between technological innovation and the principles of justice in AI-based contractual practices in Indonesia.

The rapid development of digital technology, particularly Artificial Intelligence (AI), has significantly influenced educational practices, including Islamic Religious Education (PAI). A major issue lies in how AI can be implemented effectively without undermining the spiritual, moral, and character-building values that are central to Islamic education. This study aims to examine the implementation of Artificial Intelligence in Islamic Religious Education learning in the era of school digitalization, focusing on its concepts, forms of application, teachers’ roles, benefits, as well as challenges and ethical considerations. This research employs a qualitative library research method. Data were collected from reputable academic journals, scholarly books, proceedings, and relevant official documents, and analyzed using content analysis techniques through data reduction, data display, and conclusion drawing. The findings indicate that AI has substantial potential to enhance PAI learning through personalized learning systems, adaptive learning media, and data-driven assessments. However, its implementation requires strong teacher supervision, adequate digital literacy, and alignment with Islamic values to prevent the marginalization of teachers’ moral and spiritual roles. In conclusion, Artificial Intelligence serves as a supportive tool in Islamic Religious Education and must be applied wisely, ethically, and in accordance with the fundamental objectives of Islamic education.

The development of Artificial Intelligence (AI) in the 21st century has brought significant transformation to the field of education, including Islamic Religious Education (PAI). One of the most practical implementations of this technology is the chatbot an automated conversational system capable of providing quick and contextual responses to user queries. This study aims to analyze the utilization of AI-based chatbots in addressing Islamic-related questions among students of the Islamic Education Department at IAIN Kerinci. The research employs a descriptive qualitative approach, with data collected through interviews, observations, and documentation. The findings reveal that students use chatbots as an initial source of information to understand Islamic concepts such as fiqh, tafsir, and hadith. Chatbots serve as learning aids that promote active learning and enhance students’ digital religious literacy. However, the study also identifies limitations related to the accuracy and validity of the sources used by the system, indicating that students still need verification from lecturers and authoritative Islamic literature. Overall, AI-based chatbots hold great potential to support interactive and contextual Islamic learning, provided their use is guided by academic supervision rooted in Islamic values.

The rapid development of digital technology requires teachers to possess competencies that are relevant to modern learning needs. This necessity underlies the focus of this community service program, namely transforming teacher competencies through character-based learning innovation supported by Artificial Intelligence (AI). The program was conducted at Madrasah Tsanawiyah Nurul Huda Muaro Jambi with the aim of strengthening teachers’ skills in utilizing AI as a tool to support interactive learning while instilling character values in students. The method included socialization, training, and direct mentoring in the practical application of AI-based learning media in the classroom. The results showed significant improvements in teachers’ ability to design technology-based learning materials, a better understanding of integrating character education, and increased motivation to innovate. Furthermore, the program fostered a more engaging learning atmosphere aligned with the needs of the digital generation. Therefore, the transformation of teacher competencies through AI not only enhances teacher professionalism but also plays a crucial role in shaping students with strong character amid technological advancement.

Digital transformation and advances in artificial intelligence (AI) have fundamentally changed the demands of the workplace, creating a gap between graduate competencies and industry needs. This study aims to evaluate the effectiveness of AI Agent-based career adaptability psychoeducation as a community empowerment strategy to improve the work readiness of high school/vocational school and university graduates. The study design used a descriptive-interventional approach with 27 participants who participated in a four-week online training. Data were collected through a pre-post survey using the Career Adapt-Abilities Scale (CAAS) and qualitative observations during the training. The results of the Wilcoxon Signed-Rank test showed a significant increase in career adaptability scores (Z = –4.543, p < .001), with all participants experiencing increased career adaptability. Observations showed that participants became more confident, reflective, and proactive in designing their career directions after interacting with the AI ​​Agent. These findings indicate that psychoeducational interventions integrated with intelligent technology can strengthen the adaptive capacity and work readiness of the younger generation. Theoretically, this study expands the application of the career adaptability concept in the context of AI-based learning; In practice, the results provide a relevant community empowerment model for educational and employment institutions in the era of digital disruption.

Digital transformation and advances in artificial intelligence (AI) have fundamentally changed the demands of the workplace, creating a gap between graduate competencies and industry needs. This study aims to evaluate the effectiveness of AI Agent-based career adaptability psychoeducation as a community empowerment strategy to improve the work readiness of high school/vocational school and university graduates. The study design used a descriptive-interventional approach with 27 participants who participated in a four-week online training. Data were collected through a pre-post survey using the Career Adapt-Abilities Scale (CAAS) and qualitative observations during the training. The results of the Wilcoxon Signed-Rank test showed a significant increase in career adaptability scores (Z = –4.543, p < .001), with all participants experiencing increased career adaptability. Observations showed that participants became more confident, reflective, and proactive in designing their career directions after interacting with the AI ​​Agent. These findings indicate that psychoeducational interventions integrated with intelligent technology can strengthen the adaptive capacity and work readiness of the younger generation. Theoretically, this study expands the application of the career adaptability concept in the context of AI-based learning; In practice, the results provide a relevant community empowerment model for educational and employment institutions in the era of digital disruption.

This study aims to evaluate the effectiveness of the Artificial Intelligence-Based Learning Media Development Program (P3MP-AI) in enhancing teachers’ technological and pedagogical competencies at SMK Muhammadiyah 2 Malang. The program employed a descriptive approach using both quantitative and qualitative methods, including pre-test and post-test assessments, as well as direct observation of the training process. A total of 30 teachers from various disciplines actively participated in the program conducted on August 12, 2025. The evaluation results revealed an increase in the participants’ average scores from 100 to 130 out of a maximum of 150, indicating a significant improvement in their understanding of AI concepts and applications in education. Beyond competency enhancement, the training also fostered teachers’ confidence, creativity, and ability to integrate AI-based tools into interactive learning media. However, several challenges were identified, such as limited technological resources and time constraints in classroom implementation. Overall, this program has made a tangible contribution to strengthening teachers’ digital literacy and can serve as a replicable professional development model for other vocational schools seeking to advance AI-based educational transformation.

This study evaluates the efficiency of two artificial intelligence models, DeepSeek and OpenAI, in generating code for algorithmic systems. Efficiency is assessed through execution speed, code accuracy, and the number of code characters produced. Data were collected from 100 tests covering search, sorting, graph, dynamic programming, optimization, data processing, text, and machine learning algorithms. The objective is to compare the performance of both models to support the development of efficient information retrieval systems. The method involves algorithm testing with statistical analysis of execution time, accuracy, and code length. Results indicate that DeepSeek has an average execution time of 28.74 seconds, slightly slower than OpenAI’s 28.49 seconds. However, DeepSeek’s accuracy (85.88%) surpasses OpenAI’s (85.03%). The average number of code characters is identical at 96.35 characters. The study concludes that DeepSeek excels in accuracy, while OpenAI is faster in certain cases, providing valuable insights for developers in selecting AI models for information retrieval applications.

The growing interest in learning the Japanese language in Indonesia, driven by popular culture such as anime, creates a need to understand the effectiveness of different learning media. The non-uniform effectiveness of media for each individual poses a major challenge. Therefore, this study aims to analyze the effectiveness of both anime and textbooks by segmenting learner profiles and identifying key determinants of success using an artificial intelligence approach. This research employed a quantitative method through a questionnaire survey of 120 respondents. The data were analyzed in two stages: the K-Means Clustering algorithm was used to group respondents into learner profiles, and the Decision Tree algorithm was used to identify the most significant factors that differentiate these profiles. The analysis successfully identified three distinct learner profiles: "Intensive & Adaptive Learner," "Flexible Learner," and "Passive Learner." The decision tree revealed that the perception of textbook effectiveness and the frequency of anime use are the strongest predictors in determining a learner's profile, more so than theoretical learning style preferences. It is concluded that media effectiveness is highly dependent on the learner's behavioral and perceptual profile, which underscores the importance of a personalized approach in language education technology.

Since 2022, the application of the Internet of Things (IoT) in the healthcare sector has grown significantly, marked by the increasing adoption of wearable technology, artificial intelligence (AI), machine learning (ML), and blockchain integration. Research highlights India and China as leading contributors in this domain. IoT enables real-time monitoring of chronic diseases, tracking of patient vital signs, and detection of health protocol compliance. Integrated systems such as Monit4Healthy and RADAR-IoT support personalized medical recommendations and cross-platform interoperability. However, key challenges persist, including patient data privacy and security, system interoperability issues, data fragmentation, and barriers to user acceptance due to cost, digital literacy, and device comfort. Proposed solutions include blockchain for secure data sharing, adaptive congestion control for network performance, and user training to improve technology adoption. Therefore, successful IoT deployment in healthcare requires a comprehensive approach that addresses technological, social, ethical, and sustainability aspects to achieve an effective and inclusive transformation of health services.