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Artificial Intelligence, Big Data, Cloud Computing, and Internet of Things

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... Teknologi ini dapat meniru kecerdasan manusia atau hewan, Contohnya adalah Deepblue, AlphaGo dari Google, dan Watson dari IBM, yang telah mengungguli kecerdasan manusia di bidang-bidang tertentu. Kemampuan otonomi dan automasi AI ini tergantung dari seberapa besar dan kompleks data yang dikumpulkan (big data) yang dikumpulkan melalui data mining dan di olah melalui algoritma dan machine learning (Roehl, 2022) ; (Wirtz, 2022). Semakin besar dan kompleks data yang berhasil dikumpulkan maka AI dapat menghasilkan konten/output yang sesuai dengan kebutuhan/profil/karakter dari user yang menggunakan AI. ...
... Selanjutnya, temuan manfaat dan potensi masalah penerapan AI berfokus pada bagaimana membentuk privasi dan keamanan data. AI mengandalkan data dalam jumlah besar (Big Data) yang didapat melalui proses Data Mining dan dikelola dengan algoritma tertentu dan proses machine learning (Wirtz, 2022). Data yang diperoleh dapat berupa data privat seperti, tempat dan tanggal lahir, nama, alamat, jenis kelamin, riwayat pembelian, data keuangan, dll. ...
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Penelitian ini bertujuan untuk menghasilkan rekomendasi awal tentang manfaat dan potensi masalah atas fenomena komunikasi publik yang menggunakan teknologi kecerdasan buatan atau artificial intelligence (AI). Metode penelitian yang digunakan adalah systemic literature review. Pendekatan metode penelitian review sistematis ini dilakukan berdasarkan dengan panduan PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) 2020. Hasil penelitian menunjukkan mengenai manfaat dan potensi masalah yang mungkin timbul akibat penggunaan AI dalam komunikasi publik dengan mempertimbangkan aspek teori dan fungsi komunikasi, masyarakat, dan aspek moral dan etis yang hidup di dalam masyarakat terhadap tren pada teknologi penggunaan AI dalam komunikasi publik. Penggunaan AI dalam komunikasi baik komunikasi individual maupun komunikasi publik mengakibatkan perubahan dalam pola komunikasi tradisional selama ini, dan mempunyai sisi positif dan potensi masalah dalam penggunaanya. Kemudian adanya limitasi penggunaan AI yang bertujuan agar komunikasi publik dapat berjalan dengan efektif dan kemungkinan distorsi informasi yang tersampaikan dapat termitigasi. Untuk mencapai dan membentuk masa depan yang lebih baik, penggunaan AI dalam berbagai bidang kehidupan manusia telah meningkat seiring dengan kemajuan teknologi. Ini harus dilakukan untuk mengatasi masalah etika, privasi, dan sosial. Kemajuan ini, bagaimanapun, tidak datang tanpa mempertimbangkan kemungkinan bahaya dan efeknya terhadap masyarakat dan individu. Penelitian ini memiliki signifikansi dan implikasi penting dalam konteks penggunaan kecerdasan buatan (AI) dalam komunikasi publik yaitupenelitian ini memberikan pemahaman yang lebih baik tentang manfaat dan potensi masalah yang terkait dengan penggunaan AI dalam komunikasi publik. Hal ini membantu para praktisi dan pengambil keputusan untuk memahami konsekuensi positif dan negatif yang mungkin timbul dalam penerapan teknologi ini.
... Digital government refers to adopting digital technologies to enhance public service delivery, improve administrative processes, and foster transparency and accountability. This transformation relies on cloud computing, artificial intelligence (AI), machine learning (ML), and blockchain to support efficient data management and decision-making (Wirtz, 2022). The successful implementation of digital government services requires more than technological infrastructure; it necessitates institutional readiness, regulatory frameworks, and citizen engagement (Kumar, 2024). ...
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Purpose: This study investigates the implications of digital government implementation on public sector accounting systems and data security. The purpose is to explore how digital technologies enhance financial management transparency and accountability while addressing the challenges related to data security in public administration. Research Design and Methodology: The research adopts a qualitative approach using a systematic literature review (SLR) to analyze relevant studies on digital government, accounting systems, and data security. The methodology involved reviewing recent articles and studies to understand the subject comprehensively. Findings and Discussion: The findings reveal that technologies such as cloud computing, AI, machine learning, and blockchain have significantly improved the efficiency and accuracy of public sector financial reporting. Blockchain enhances transparency by ensuring the immutability of financial transactions. However, the study also highlights challenges such as cybersecurity risks, data breaches, and the need for comprehensive data governance frameworks. The findings emphasize the importance of human resources preparedness and infrastructure readiness to implement digital accounting systems effectively. Implications: The study’s practical implications suggest that policymakers must invest in secure digital infrastructure, provide continuous training for government employees, and establish strong data governance frameworks. These strategies are critical for ensuring digital government systems' effectiveness, security, and accountability.
... This period of disruption is characterized by the fusion of digital technologies, physical systems, and the integration of various industries, leading to significant transformations across sectors. The industrial revolution 4.0 builds upon the foundations of previous industrial revolutions but distinguishes itself through the convergence of technologies such as artificial intelligence, robotics, the Internet of Things (IoT), big data analytics, and automation [1], [2] . These technologies have the potential to revolutionize industries, economies, and societies on a global scale. ...
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Digital media and business are rapidly growth. IMNU has a willingness to improve digital individuals' skills. They try to campaign using social media platforms and face-to-face workshops. explore the role of IMNU in implementing strategic campaigns of digital and business media. The research is a descriptive qualitative study in which participant observation is the primary data. The result shows that IMNU campaign consists of three categories: training, tutorials, and practitioners' sharing experiences on social media. They also present face-to-face digital training or workshop in NU-based Islamic boarding schools throughout Indonesia. The existence of IMNU has become important to help Islamic youth in developing their digital skill and accompanying them to provide knowledge on digital literacy
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The artificial intelligence (AI) has been a platform of immense assistance to develop and simplify discoveries in medical science. However, the environmentalist have been researching this concept to benefit the environment to establish multidimensional discoveries of clean energy. An increase in greenhouse gases (GHGs) is caused by most human developmental activities. Direct or indirect emission of GHGs by person, group, event, or any other activity contributes to the carbon footprint. According to the Environmental Protection Agency, USA, major sources of increasing GHGs are transportation (29%), electricity (28%), industry (22%), commercial and residential (12%), and agriculture (9%). Vigorous effects are required to control the increasing GHGs by developing and implementing policies and utilizing new technologies. In this time of challenges presented by climate change, technological advancements in artificial intelligence (AI) or digital assistance have made a significant impact on people’s lifestyles. AI-based technologies to monitor, predict, and reduce GHGs emissions may help in a cleaner environment. This article aims to describe different AI-based approaches to minimize carbon footprints as well as discuss the role of AI in various industries and its economic and societal outcomes. Specifically, we have attempted to fill the research gaps by investigating existing opportunities in the field of AI toward reducing GHG emissions.
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The usage of lithium-ion batteries has significantly increased by various applications in recent years due to the advantages of long lifespan, high energy density, high power density, and eco-friendly environment benefits for sustainable usage. Although it has attracted much interest on its manufacturing process from practitioner in Industry 4.0 now, academia has relatively less concern on addressing the manufacturing process in using hybrid techniques. Thus, an advanced hybrid four-stage detection model is proposed into the original process to help identify optimal real-time production performance to hold sustainable manufacturing. This proposed hybrid model creates four key detection stages corresponded to four core challenges, including autoregressive integrated moving average (ARIMA), If-Then-Else programming rule, convolutional neural networks (CNNs), and artificial neural networks (ANNs) for achieving the purpose of economizing manpower and material resources to save manufacturing cost. (1) In cyclic process stage, ARIMA makes a successful prediction of a 20-min discharging curve with a significant low 13.38% error rate. (2) If-Then-Else programming rule sets up min–max threshold range (MMTR) on both attributes of voltage and capacity to find out failure packs before final test 1 and benefit with saving manual testing time. (3) CNNs achieve 100% classification accuracy in a productive result and save average 3.5 h for each battery pack. (4) ANNs conclude an empirical result of 100% accuracy in predicting the battery pack pass or failure. Conclusively, the study makes a comparative research with various deep learning algorithms to evaluate its performance; the proposed hybrid detection model is never seen in the challengeable lines of predicting Li-ion battery pack, and thus, it has an innovative value and priority performance for benefiting the sustainable manufacturing on offering green and renewable energy. This study contributes the research rationality and practical significance.
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Wir nennen uns selbst Homo sapiens – der weise Mensch. Erste Versuche, diese Weisheit zu beschreiben, zu verstehen, abzubilden und in Gesetzmäßigkeiten zu verwandeln, reichen bis in die Antike zurück und haben eine lange Tradition in der Philosophie, Mathematik, Psychologie, Neurowissenschaft und Informatik. Vielfach wurde versucht, den Begriff der Intelligenz – also die kognitive Leistungsfähigkeit des Menschen – besser zu verstehen und zu definieren. Als KI bezeichnet man traditionell ein Teilgebiet der Informatik, das sich mit der Automatisierung von intelligentem Verhalten befasst. Eine genaue Begriffsbestimmung ist jedoch kaum möglich, da auch alle direkt verwandten Wissenschaften wie Psychologie, Biologie, Kognitionswissenschaft, Neurowissenschaft an einer genauen Definition von Intelligenz scheitern.
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A key component of human intelligence is our ability to think about each other's mental states. This ability provides an interesting challenge for cognitive neuroscience at- tempts to understand the nature of abstract concepts and how the brain acquires them. Research over the past 15 years has shown that very young children and children of extremely lim- ited intellectual ability can acquire mental state concepts with ease. Children with Kanner's syndrome have severe difficulty using these concepts, despite relatively great experience and ability. These discoveries have led to the development of the first information processing models of belief-desire reasoning. The term "theory of mind" was coined by David Pre- mack (Premack and Woodruff, 1978) to refer to our ability to explain, predict, and interpret behavior in terms of mental states, like wanting, believing, and pretending. Because the behavior of complex organisms is a result of their cognitive properties— their perceptions, goals, internal information structures, and so on —it may have been adaptive for our species to develop some sensitivity to these properties. The capacity to attend to mental state properties is probably based on a specialized represent- ational system and is evident even in young children. The term "theory of mind" is potentially misleading. It might suggest that the child really has a theory or that the child has a theory of mind as such. Although there are some writers who hold such views (Perner, 1991; Gopnik and Meltzoff, 1997; Gopnik and Wellman, 1995), I assume simply that the child is endowed with a representational system that captures cognitive proper- ties underlying behavior. To better see what is meant by "theory of mind" ability, consider the following scenario (figure 85.1). Sally has a marble that she places in a bas- ket and covers, and then departs. While she is gone, Ann removes the marble from the basket and places it in the box. A child to whom this scenario is presented then is asked to predict where Sally will look for her marble when she returns. To correctly predict Sally's be- havior, it is necessary to take into account both Sally's desire for the marble and Sally's belief concerning the location of the marble. In this scenario, Sally's belief is
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Software-as-a-Service (SaaS) helps organizations avoid capital expenditure and pay for the functionality as an operational expenditure. Though enterprises are unlikely to use SaaS model for all their information systems needs, certain business functionalities such as Sales Force Automation (SFA), are more seen to be implemented using SaaS model. Such demand has prompted quite a few vendors to offer SFA functionality as SaaS. Enterprises need to adopt an objective approach to ensure they select the most appropriate SaaS product for their needs. This paper presents an approach that makes use of Analytic Hierarchy Process (AHP) technique for prioritizing the product features and also for expert-led scoring of the products.
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This article can be viewed as an attempt to explore the consequences of two propositions. (1) Intentionality in human beings (and animals) is a product of causal features of the brain. I assume this is an empirical fact about the actual causal relations between mental processes and brains. It says simply that certain brain processes are sufficient for intentionality. (2) Instantiating a computer program is never by itself a sufficient condition of intentionality. The main argument of this paper is directed at establishing this claim. The form of the argument is to show how a human agent could instantiate the program and still not have the relevant intentionality. These two propositions have the following consequences: (3) The explanation of how the brain produces intentionality cannot be that it does it by instantiating a computer program. This is a strict logical consequence of 1 and 2. (4) Any mechanism capable of producing intentionality must have causal powers equal to those of the brain. This is meant to be a trivial consequence of 1. (5) Any attempt literally to create intentionality artificially (strong AI) could not succeed just by designing programs but would have to duplicate the causal powers of the human brain. This follows from 2 and 4. “Could a machine think?” On the argument advanced here only a machine could think, and only very special kinds of machines, namely brains and machines with internal causal powers equivalent to those of brains. And that is why strong AI has little to tell us about thinking, since it is not about machines but about programs, and no program by itself is sufficient for thinking.
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