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Background: The scheduling of work of health workers, especially radiographers in type B hospitals, is a complex challenge due to the variety of radiology modalities, variations in the number of human resources, and the provisions of working hours regulations from the Ministry of Health of the Republic of Indonesia. Manual scheduling that is still in use tends to cause workload inequality, conflicts between employees, and operational inefficiencies. Objective: This study aims to design and develop an Artificial Intelligence (AI)-based radiographer shift scheduling system that is able to prepare work schedules automatically, fairly, flexibly, and integratedly, in accordance with hospital service regulations and needs. Research Method: This type of research is Research and Development (R&D). The development process is carried out through the stages of needs analysis, designing Python and Flask-based systems, simulating tests on data, and expert validation then the data collected and described from the initial mapping and also mapping potential problem-solving. Results: The system successfully manages morning, noon, night, and holiday shift schedules based on competence, fair rotation, and maximum working hours provisions. By showing a significant difference between user perceptions before and after using the system, which reflects improved efficiency, fairness, and ease of access to schedules. Respondents expressed satisfaction with the override feature and integrated notifications. Conclusion: The development of an AI-based radiographer shift scheduling system has proven to be feasible and effective in overcoming managerial problems of work scheduling in hospitals. This system is able to increase efficiency, transparency, and user satisfaction, and has the potential to be widely applied to various types of hospitals in Indonesia.

In today's digital era, the need for fast, accurate, and responsive information systems is increasingly pressing, especially in the higher education sector where prospective students often face obstacles in directly obtaining relevant and reliable academic information. To address this challenge, this research focuses on the design and development of an academic service chatbot by implementing the Retrieval-Augmented Generation (RAG) method at Catur Insan Cendekia University. The RAG approach combines information retrieval capabilities from various documents (retrieval) with the generative intelligence of language models, thus being able to produce contextual, personalized, and data-driven answers. The chatbot system was developed using Python, LangChain, FAISS, and the GPT model as the core of natural language processing. Performance evaluation was conducted using the ROUGE metric, which showed quite good results with a ROUGE-1 value of 0.50 and a ROUGE-L of 0.48. These findings prove that the system is capable of providing relevant and high-quality responses in helping answer prospective students' academic questions. With these advantages, this chatbot is expected to be an innovative solution to improve the quality of academic information services at UCIC, as it can present data quickly, accurately, interactively, and automatically. Furthermore, the implementation of this artificial intelligence-based technology aligns with digital transformation efforts in higher education, supporting the efficiency of academic services and strengthening the institution's image as a modern campus that adapts to developments in information technology.

Chat Generative Pretrained Transformer (ChatGPT) has rapidly gained popularity and is increasingly utilized across various fields, including education, where it plays a significant role as a supporting tool for academic tasks. In the educational context, ChatGPT can assist students in preparing and completing a wide range of assignments, providing quick access to structured information and alternative perspectives. This study was designed to explore the views of both students and lecturers on the integration of ChatGPT in academic activities, specifically as a support system for handling student assignments. Employing a descriptive qualitative approach, data were collected through interviews with 5 students and 6 lecturers from the Business Education Study Program at the State University of Surabaya. The findings reveal contrasting yet complementary perspectives. From the students’ point of view, ChatGPT offers considerable benefits, particularly in enhancing independence, efficiency, and time management when working on lecture assignments. However, these advantages are accompanied by drawbacks, such as decreased creativity, reduced critical thinking, and diminished interest in consulting traditional learning resources like books. On the other hand, lecturers acknowledge the usefulness of ChatGPT in simplifying student work and accelerating the completion of assignments but express concerns regarding the overreliance on artificial intelligence, which could potentially hinder the development of essential academic skills. Overall, the research suggests that while ChatGPT presents valuable opportunities to enhance learning processes, it must be applied thoughtfully, with careful guidance from educators to balance efficiency with the cultivation of creativity, critical thinking, and academic integrity.

Artificial Intelligence (AI) has significantly transformed the design industry by introducing innovative tools that enhance efficiency, creativity, and personalization in the design process. This article explores the impact of AI technologies on the practice of modern designers, from the early stages of idea generation to the final execution of projects. By analyzing case studies, conducting surveys, and reviewing recent literature, the study reveals that AI functions not only as a tool but also as a collaborator that expands the boundaries of human creativity. It allows designers to explore new possibilities and push the limits of traditional design processes. However, the integration of AI into design practices also raises important ethical and professional challenges, such as concerns about job displacement, data privacy, and the potential loss of human touch in the creative process. Despite these concerns, the findings highlight that embracing AI is crucial for designers to remain competitive and sustain their careers in the rapidly evolving digital era. Adapting to AI technologies, understanding their capabilities, and effectively incorporating them into creative workflows will enable designers to stay relevant and thrive in a landscape where technology continues to reshape the industry. This study provides insights into the evolving relationship between human designers and AI, offering recommendations for future research and practice in the field of design.

This study investigates the effectiveness of virtual debate platforms in enhancing English as a Second Language (ESL) learners’ critical thinking, speaking fluency, and argumentation skills. The research was conducted using a quasi-experimental design with intermediate-level ESL students as participants. Data were collected through critical thinking tests, speaking fluency rubrics, and argumentation rubrics, complemented by qualitative analysis of recorded debates. The results indicate significant improvements in students’ critical thinking abilities, fluency in spoken English, and the quality of their arguments, including structure, relevance of evidence, and coherence. Furthermore, virtual debates fostered higher levels of student engagement compared to traditional classroom discussions. These findings underscore the pedagogical potential of integrating technology-based debate activities into ESL instruction, providing interactive and collaborative learning opportunities that address the limitations of conventional teaching methods. The study concludes with recommendations for incorporating virtual debates into ESL curricula and suggests future research on the use of emerging technologies such as artificial intelligence (AI) and virtual reality (VR) to further enhance language learning experiences.

This article examines the economic defense reforms implemented by China under Xi Jinping's leadership through a literature review. This research aims to examine the key strategies employed by the Chinese government to strengthen its economic resilience amidst global pressures, such as the trade war with the United States, the COVID-19 pandemic, and escalating geopolitical rivalries. The findings indicate that Xi Jinping prioritizes a dual-circulation strategy, namely strengthening domestic consumption while maintaining connectivity with international markets, as a crucial step in addressing global uncertainty. Furthermore, the Made in China 2025 policy serves as a key pillar for promoting technological independence, reducing dependence on imports, and enhancing the competitiveness of domestic industries in strategic sectors such as information technology, renewable energy, and artificial intelligence. Furthermore, through the Belt and Road Initiative (BRI), China seeks to expand its economic influence globally, open new markets, and transfer excess domestic industrial capacity to partner countries. These reforms are not solely oriented toward economic aspects but are also closely linked to political and national security interests. This strategy demonstrates China's systematic efforts to build economic resilience integrated with geopolitical interests, using the economy as an instrument of both soft and hard power. This study demonstrates that Xi Jinping's economic defense reforms are not merely a short-term response to the global crisis, but rather part of a long-term vision to establish China as a dominant global power. Thus, this study emphasizes the importance of understanding China's economic strategy holistically, as these reforms, in addition to significantly influencing domestic dynamics, also have significant implications for the future global economic and political order.

Limited knowledge and utilization of digital technology in the fisheries and plantation sectors remains a major obstacle for the people of Samo Village, South Halmahera Regency, the majority of whom work as fishermen and farmers. This results in low productivity, limited access to information, and less than optimal development of micro-enterprises based on local potential. This community service activity aims to introduce and train the use of Fishbrain, Plantix, and ChatGPT applications relevant to local community needs. The activity implementation method includes three main stages: the preparation phase, which involves coordination with village officials and the development of training materials; the implementation phase, which involves socialization and hands-on training; and the evaluation phase, which involves discussions, reflections, and Q&A sessions with participants. The results of the activity demonstrated a significant increase in participants' understanding and skills in utilizing digital technology. The Fishbrain application has been proven to assist fishermen in determining more potential fishing locations based on global community data, while Plantix is ​​useful in detecting plant diseases, providing treatment recommendations, and increasing crop yields. Meanwhile, ChatGPT is used by the community as a virtual assistant to support information searches, innovation ideas, product marketing strategies, and digital-based small business planning. The participants' enthusiasm was evident in their active involvement throughout the training, their eagerness to try the applications independently, and their ability to repeat the three applications' core functions. In conclusion, this training activity not only broadened the community's digital knowledge and skills but also encouraged a shift in mindset toward utilizing technology as a means to improve well-being. With the knowledge gained, the Samo Village community is expected to be more adaptive to technological developments, enabling the sustainable development of the fisheries, plantations, and local micro-enterprises sectors.

Dr. Soedirman Kebumen Regional General Hospital has implemented PACS to replace traditional film, but limitations in IT infrastructure, RME integration, and human resource readiness require an integrated implementation model that combines cloud-hybrid, DICOM/HL7 with SSO, continuous training, and managerial support. To evaluate the implementation of the PACS system in the Radiology Department of Dr. Soedirman General Hospital in Kebumen and to analyze the factors that support and hinder the effectiveness of the PACS system in improving the quality of radiology services. The research used a qualitative approach with an interactive model. Data collection was conducted through in-depth interviews, Focus Group Discussions (FGD), direct observation, and documentation. The data obtained were analyzed using ATLAS.ti software to explain the PACS implementation and its impact on the effectiveness of radiology services. The PACS implementation improved the quality of radiology services by accelerating access to medical images and enhancing workflow efficiency. Some challenges, such as system downtime, integration with other systems, and technical limitations, need to be addressed. Integration of artificial intelligence (AI) and telemedicine technologies needs to be enhanced to achieve optimal radiology services. Factors supporting successful implementation include the adoption of advanced technologies (cloud computing and AI), adequate infrastructure, technical support from the IT team, and strong managerial commitment.  Barriers to success include imperfect system integration, power outages, downtime, storage capacity limitations, and a shortage of trained human resources. Proposed implementation models include improving PACS system infrastructure, developing ongoing training for staff, improving PACS system integration with other hospital systems, and improving interdepartmental communication to streamline workflows and reduce obstacles in the diagnostic process.

The digital competency gap between vocational school graduates and the rapidly evolving demands of modern industry has become a pressing issue in today’s workforce. One of the main challenges lies in the limited digital literacy and insufficient practical skills of students, particularly in applying Artificial Intelligence (AI) tools to support productivity and creativity. This situation hinders the readiness of graduates to compete in an AI-driven labor market, where innovation and technological adaptability are essential. To address this challenge, a training program was designed for vocational high school students at Darma Siswa Sidoarjo with the primary objective of enhancing both their conceptual understanding and practical competencies in the utilization of AI technologies. The program adopted the Participatory Action Research (PAR) method, emphasizing collaboration and active engagement between trainers and students. It was conducted through a series of interactive workshops that combined theoretical introductions with hands-on practice. Students were guided to explore AI-based platforms such as Ideogram for visual creativity, PixVerse for multimedia production, and Suno for AI-generated music. To evaluate the effectiveness of the program, pre-test and post-test instruments were used, focusing on the dimensions of knowledge, technical skills, and student confidence in using AI tools. The findings revealed that students initially demonstrated minimal awareness and limited practical ability in AI applications, as reflected in the pre-test results. However, post-test data showed a significant improvement not only in their technical proficiency but also in their enthusiasm to further explore AI technologies. Moreover, students gained increased confidence in producing creative outputs, transforming their role from passive consumers of digital tools into active and competent content creators. In conclusion, this participatory and practice-oriented training model effectively bridges the digital competency gap, enhances students’ job readiness, and fosters an innovative mindset.

This study aims to explore how users' perceptions of the chatbot on the Shopee platform influence user satisfaction and loyalty. The research employs a qualitative approach with in-depth interviews to gather users' experiences and views on their interactions with the chatbot. The findings reveal that three key factors—clarity of responses, ease of access, and relevance of information—are crucial in determining user satisfaction. Users who found the chatbot's responses clear and easily understood, along with quick access, were generally more satisfied with the service provided. However, despite achieving user satisfaction, loyalty to the platform has not been fully established. This is due to the chatbot's limitations in building emotional connections with users and its inability to handle more complex issues. While the chatbot is effective in providing automated solutions for simple problems, it lacks the empathy and personalized attention necessary when users encounter more intricate issues or require a deeper level of interaction. These findings highlight that although chatbots can enhance the efficiency of basic customer service, they cannot fully replace human interaction, particularly in customer service contexts that require emotional engagement and a deeper understanding of the user's needs. To foster stronger user loyalty, a combined approach involving both technology and human interaction is essential. The chatbot can address routine issues, while more complex matters should be referred to customer service representatives who can offer empathetic and tailored solutions. The recommendation from this study is for Shopee platform developers and sellers to strategically optimize chatbot use while maintaining human interaction that is adaptable, in order to enhance user satisfaction and loyalty.

The increasing number of mental health disorders in various countries has created an urgent need for innovation in the diagnosis and treatment process. This problem not only impacts individuals' quality of life but also creates a significant social and economic burden. One solution that is beginning to be widely researched is the use of artificial intelligence (AI) in the field of mental health. This research used a literature review of various previous studies discussing the role, application, and impact of AI. The results of the review indicate that AI technology, particularly in the form of digital applications such as chatbots, has great potential to support the recovery process for patients with mental disorders. AI-based chatbots can provide responsive, two-way interactions, so users feel heard and receive initial emotional support. One technical approach used is Natural Language Processing (NLP), which enables the system to understand natural human language. Simultaneously, Long Short-Term Memory (LSTM) algorithms are used to analyze language patterns and detect symptoms of depression more accurately. Various studies have reported that the application of NLP and LSTM can improve the reliability of diagnoses and provide responses tailored to user needs. Furthermore, AI can provide personalized recommendations, tailor interventions to the user's condition, and monitor mental health developments in real time. This has the potential to assist mental health practitioners in making faster and more informed decisions. However, the adoption of AI among practitioners remains relatively low. Influencing factors include limited technological understanding, limited infrastructure, and debates over ethical aspects and data privacy. Therefore, while AI has significant potential to improve the quality of mental health services, regulations, ethical guidelines, and synergy between technology and healthcare professionals are needed to ensure safe and effective implementation.

This paper explores the transformative influence of artificial intelligence (AI) on the accounting profession, particularly within the Accountant General of the Federation (OAGF). The research investigates how AI-driven innovations are reshaping traditional accounting practices and redefining the role of accountants. By conducting a systematic literature review, this study identifies three primary dimensions of AI’s impact: the automation of repetitive tasks such as data entry, transaction processing, and reconciliation; enhanced data analytics capabilities, which include predictive modeling and real-time decision support; and the evolution of accountants' roles toward more strategic and value-added activities, such as financial advisory and risk management. The automation of routine processes through AI allows accountants to focus on higher-level tasks that require judgment, creativity, and expertise, ultimately enhancing the overall efficiency of the accounting function. Furthermore, AI’s advanced data analytics tools provide more accurate insights, enabling accountants to offer more effective financial guidance and make more informed decisions. As AI reduces the time spent on manual processes, accounting professionals can improve their role in advising on business strategy, improving risk management, and identifying new growth opportunities. The study’s findings underscore the importance of embracing AI in the accounting profession, not only to improve operational efficiency, reduce costs, and scale operations but also to enable accountants to stay competitive in a rapidly evolving technological landscape. The paper concludes by emphasizing that adopting AI is essential for accountants to remain relevant and continue providing valuable contributions to their organizations. Future research should focus on the long-term implications of AI on accounting ethics and the development of necessary skills for accounting professionals to thrive in the age of AI.

This study aims to deeply examine the role and impact of AI implementation in the B2B sales process, including implementation challenges. The method used is a qualitative approach with descriptive studies, through a literature review of scientific journals, industry reports, and relevant previous studies. The analysis results show that the application of AI in B2B sales not only improves the accuracy of marketing strategies and lead conversion, but also strengthens the concept of value co-creation through collaboration between salespeople and AI systems. Technologies such as machine learning, predictive analytics, and NLP-based chatbots have been proven to accelerate sales cycles, expand service reach, and increase productivity by up to 30%. However, implementation challenges remain, including limited digital infrastructure, a lack of competent human resources, and organizational resistance to technological transformation. Therefore, optimal AI integration requires institutional readiness, adaptive strategies, and continuous investment in technology and human resource development. These findings provide theoretical and practical contributions to the development of AI-based B2B sales strategies, particularly in the context of the digital industry in Indonesia. Furthermore, the application of AI in B2B sales also opens up new opportunities for service personalization. With the support of real-time data analysis, companies can better understand the specific preferences and needs of their business partners. This enables the development of more targeted communication strategies and the enhancement of long-term relationships with business customers. AI also plays a role in reducing human error through automated systems that can validate data, provide predictive recommendations, and support faster and more accurate strategic decision-making. Furthermore, the adoption of AI in B2B requires clear regulations and governance, particularly regarding ethical data use and information security. Companies must be able to balance the use of technology to increase efficiency with the protection of privacy and consumer rights.

The agricultural sector plays a crucial role in fulfilling food needs, but it continues to face several challenges, including limited land, climate change, and the need for more cost-efficient production methods. To address these challenges, the application of Artificial Intelligence of Things (AIoT) technology emerges as a promising solution to enhance productivity and efficiency in farm management. AIoT technology integrates artificial intelligence (AI) and the Internet of Things (IoT), enabling smarter decision-making, automation, and real-time data analysis in agricultural practices. This community service activity aimed to provide AIoT technology training to the Satria Tani Hanggawana Farmers' Group in Kalisapu Village, Slawi Sub-district, Tegal Regency, focusing on premium melon cultivation. The methods used in the activity included presentations on relevant materials, an introduction to AI and IoT-based Smart Farming technology, hands-on practice in a greenhouse, and interactive discussions. These methods were designed to equip participants with practical knowledge about integrating AIoT technology into their agricultural operations, enabling them to apply it directly to melon cultivation. The results of the activity showed a significant increase in the participants' understanding of agribusiness management, basic bookkeeping practices, and the application of AIoT in melon cultivation. The participants exhibited high enthusiasm, actively engaging in discussions and successfully performing practical exercises. This training was effective in improving farmers' knowledge and motivating them to adopt AIoT technology in their farming operations. The program has been instrumental in encouraging the use of smart farming techniques, which can lead to more efficient and sustainable farming practices. It is recommended that ongoing support, including follow-up activities and further training, be provided to ensure the sustainability of the technology implementation and continue to improve local food security

In today's digital era, technological development is occurring rapidly and having a significant impact on various aspects of human life, including Islamic da'wah. The transformation of the spread of Islamic teachings in the digital age has become inevitable along with the rapid advancement of information technology, particularly with the advent of Artificial Intelligence (AI). This study aims to explore how AI is utilized in spreading Islamic values and analyze the accompanying ethical and theological challenges. This research uses a qualitative approach with a literature study method, which includes a review of scientific journals, articles, and contemporary Islamic documents. The results show that AI has been applied in various forms of digital da'wah. For example, Islamic chatbots that can automatically answer religious questions, prayer and other worship reminder applications, and Quran learning platforms equipped with voice recognition and tajweed recitation technology. Although innovative and promising, the use of AI in da'wah poses challenges, particularly the threat to authority over scientific knowledge, the validity of religious content, and the risk of dehumanization in the process of conveying Islamic teachings. Therefore, an adaptive yet ethical approach to Islamic preaching is needed, involving collaboration between religious scholars, academics, and technology experts. This approach must take into account the values of Islamic law, morality in digital communication, and sensitivity to the cultural context of Muslims. Thus, AI-based Islamic preaching can be an innovative solution for effectively reaching a wider audience, without sacrificing the essence and spirituality taught by Islam itself. Thus, AI-based Islamic preaching can be an innovative solution for effectively reaching a wider audience, without sacrificing the essence and spirituality taught by Islam itself. Going forward, a sustainable Islamic digital ecosystem needs to be built so that technology becomes not only a tool, but also a means of increasing faith and piety.

This study aims to utilize data mining techniques, specifically the K-Nearest Neighbors (KNN) algorithm, to classify leaf diseases in sugarcane (Saccharum officinarum). Early and accurate detection of leaf disease types is a crucial step in prevention and control strategies, thereby reducing potential crop losses caused by pathogen attacks. Leaf diseases in sugarcane, such as leaf scald, rust, and mosaic virus, are known to affect photosynthesis, inhibit growth, and reduce the quality and quantity of sugarcane produced. The classification process in this study was carried out through image analysis of infected sugarcane leaves, where features such as color, texture, and shape were extracted using digital image processing techniques. The KNN algorithm was chosen because of its non-parametric nature, ease of implementation, and its ability to provide accurate classification results even with limited data size. The working principle of KNN is to determine the class of a new sample based on the majority class of its k nearest neighbors in the feature space, making it very suitable for the case of leaf disease image classification. In addition to building a classification model, this study also examines disease prevention strategies based on the identification results. These strategies include the use of disease-resistant sugarcane varieties, the implementation of appropriate planting patterns, land moisture management, regular plantation sanitation, and the measured and environmentally friendly use of pesticides or fungicides. Model performance evaluation was conducted using accuracy, precision, recall, and F1-score metrics to assess model effectiveness across various data scenarios. The results of this study are expected to not only contribute to the development of decision support systems for farmers and related parties but also support the application of artificial intelligence-based technology in the agricultural sector.

The development of information and communication technology (ICT), particularly the use of Artificial Intelligence (AI), the Internet of Things (IoT), and big data, has transformed Indonesia's national security threat landscape. Threats that previously focused on traditional military aspects have now shifted to non-traditional cyber threats, such as cyberattacks, digital espionage, and infrastructure sabotage. According to PROXSIS IT GRC data, in 2024, more than 19 million cyberattacks were recorded against websites in Indonesia. While this figure is a decrease compared to the previous year, this trend is thought to reflect a shift towards more structured and organized tactics by threat actors. In response, the Indonesian government established the National Cybersecurity Action Plan 2024–2028 as a strategic guideline. Furthermore, cyber diplomacy is being promoted through bilateral and multilateral cooperation, for example through the signing of memorandums of understanding (MoUs) with the UK and Kaspersky. This cooperation includes the exchange of intelligence information, strengthening human resource capacity, raising public awareness regarding cybersecurity, and protecting critical information infrastructure. This study uses non-traditional security theory and defense diplomacy to analyze the strategies, challenges, and prospects of Indonesia's cyber policy. The analysis demonstrates that cyber defense diplomacy plays a crucial role as an instrument for integrating technology, regulation, and international collaboration in safeguarding digital sovereignty. In addition to strengthening threat detection and mitigation capabilities, this diplomacy also builds networks of trust with partner nations, which is essential amidst the increasing complexity of global threats. Therefore, in the era of digital globalization, full of interconnections, cyber defense diplomacy serves not only as a national protection tool but also as Indonesia's contribution to global cybersecurity stability. This effort prioritizes synergy between technological innovation, law enforcement, and sustainable international cooperation.

Number recognition is a critical skill in early education, laying the foundation for digital literacy and mathematical understanding. However, traditional methods of teaching number recognition often lack the interactivity and engagement necessary for effective learning, particularly for young learners. This study proposes the development of a deep learning-based Android application aimed at enhancing the number recognition process by providing an interactive and visual learning experience. The application utilizes a Convolutional Neural Network (CNN), a type of deep learning model, to recognize handwritten numbers, offering users an innovative and engaging way to learn and practice number recognition.In the proposed application, users can draw numbers on their devices, and the system will immediately provide feedback regarding the accuracy of the drawn numbers. The CNN model will be trained on a comprehensive dataset of handwritten digits to ensure high accuracy in recognition. This real-time feedback loop is designed to help users learn the correct form of numbers while also introducing the foundational concepts of deep learning. The main objective of this application is to provide an accessible, interactive platform for learning number recognition, especially for novice learners who may be unfamiliar with basic concepts in machine learning and digital literacy. By integrating deep learning technology, the application not only supports the learning of number recognition but also serves as an introduction to artificial intelligence (AI) concepts in a practical, easy-to-understand format. Furthermore, the application is designed to be user-friendly, ensuring that it is suitable for a wide range of learners, including children and beginners. The application aims to combine the fundamental principles of deep learning with a practical, hands-on learning experience, fostering a deeper understanding of both number concepts and the potential of AI in everyday life.

The development of digital technology has brought about significant changes in the way humans communicate and live their daily lives. One important innovation is Artificial Intelligence (AI), one tangible manifestation of which is the chatbot as a virtual friend. This study aims to examine the role of chatbots as virtual friends through a literature review approach. The results of the study indicate that chatbots can serve as a potential, easily accessible tool for providing emotional support. Chatbots are able to create a safe space for users, especially Generation Z, to express themselves and overcome loneliness. However, the relationship between humans and chatbots is complex. Chatbots can mimic supportive responses, but as algorithmic systems, their ability to experience true empathy is very limited. Users' awareness that responses are coming from a machine also influences perceptions of trustworthiness and the quality of the interaction. On the other hand, the use of chatbots also raises serious ethical challenges, such as data privacy issues, the potential for over-dependence, and increased loneliness if chatbots are used as a substitute for real social interaction. Therefore, the development and use of chatbots as virtual friends must be carried out with a critical understanding and ethical approach. Technology design oriented towards humanitarian values is needed so that the presence of chatbots does not diminish the essence of human relationships but instead becomes a psychosocially beneficial complement.

This research was conducted from June to July 2025 in Binjai City, with the primary focus being analyzing the readiness of the Binjai City Regional Disaster Management Agency (BPBD) to implement a flood early warning system utilizing artificial intelligence (AI). The data collection process was conducted through a literature review, which involved reviewing various theories and previous research results regarding the application of AI and Internet of Things (IoT) technology in the context of disaster mitigation. Based on the results of the study, it was found that the use of technologies such as ultrasonic sensors, microcontrollers, fuzzy logic, and automatic notification systems can provide real-time warnings with a high level of accuracy and a fast response. This system enables early detection of rising river levels through automatic measurements, intelligent data processing, and sending notifications to authorities and affected communities within seconds. By integrating historical data and machine learning-based predictions, this system is also able to depict potential flooding before it occurs, providing a longer response time for evacuation. However, the readiness of the Binjai City BPBD still faces various challenges, such as limited digital infrastructure, the need for human resource training in the technology field, and inadequate budget allocation. Therefore, cross-sector collaboration and ongoing policy support are needed for optimal implementation of this system. The use of AI and IoT in early warning systems is not only technically relevant but also urgent in the face of increasing climate change and flood risks. A strategy involving cross-sector collaboration between government, academia, and the private sector is needed to develop an adaptive and sustainable early warning system.