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"Generative Artificial Intelligence (GAI) telah memegang penting dalam berbagai bidang, termasuk sebagai alat bantu pemrograman di Indonesia. Namun, penelitian mengenai adopsi GAI sebagai alat bantu pemrograman masih terbatas. Penelitian ini bertujuan menganalisis faktor yang memengaruhi niat karyawan di Indonesia untuk mengadopsi GAI dalam pemrograman, dengan fokus pada kualitas output kode dan kualitas sistem yang memengaruhi persepsi kegunaan serta kemudahan penggunaan GAI. Penelitian menggunakan metode PLS-SEM dalam analisis kuantitatif dengan 497 data valid, serta analisis kualitatif melalui wawancara 10 narasumber. Hasilnya menunjukkan bahwa persepsi kegunaan dipengaruhi oleh faktor presentation, structure, interactivity, responsiveness, understandability, assurance, dan reliability, sementara persepsi kemudahan penggunaan dipengaruhi oleh presentation, structure, responsiveness, assurance, dan reliability. Kedua persepsi ini memengaruhi niat adopsi GAI untuk pemrograman. Penelitian juga meneliti hubungan ini berdasarkan gender dan usia melalui analisis multigrup. Hasilnya memberikan saran bagi pengembang GAI untuk meningkatkan kualitas kode output dan sistem, yang terbukti memengaruhi persepsi pengguna tentang kegunaan dan kemudahan penggunaan GAI

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Generative Artificial Intelligence (GAI) has become significant in various fields, including as a programming aid in Indonesia. However, research on the adoption of GAI as a programming tool remains limited. This study aims to analyze the factors influencing employees in Indonesia to adopt GAI for programming, focusing on output code quality and system quality, which affect the perceived usefulness and ease of use of GAI. The study employs the PLS-SEM method for quantitative analysis with 497 valid data points and qualitative analysis through interviews with 10 informants. The results indicate that perceived usefulness is influenced by factors such as presentation, structure, interactivity, responsiveness, understandability, assurance, and reliability, while perceived ease of use is influenced by presentation, structure, responsiveness, assurance, and reliability. Both perceptions affect the intention to adopt GAI for programming. The study also examines these relationships based on gender and age using multigroup analysis. The findings provide practical suggestions for GAI developers to enhance the quality of output code and system, which significantly influence users' perceptions of the usefulness and ease of use of GAI."

"Generative Artificial Intelligence (GAI) telah memegang penting dalam berbagai bidang, termasuk sebagai alat bantu pemrograman di Indonesia. Namun, penelitian mengenai adopsi GAI sebagai alat bantu pemrograman masih terbatas. Penelitian ini bertujuan menganalisis faktor yang memengaruhi niat karyawan di Indonesia untuk mengadopsi GAI dalam pemrograman, dengan fokus pada kualitas output kode dan kualitas sistem yang memengaruhi persepsi kegunaan serta kemudahan penggunaan GAI. Penelitian menggunakan metode PLS-SEM dalam analisis kuantitatif dengan 497 data valid, serta analisis kualitatif melalui wawancara 10 narasumber. Hasilnya menunjukkan bahwa persepsi kegunaan dipengaruhi oleh faktor presentation, structure, interactivity, responsiveness, understandability, assurance, dan reliability, sementara persepsi kemudahan penggunaan dipengaruhi oleh presentation, structure, responsiveness, assurance, dan reliability. Kedua persepsi ini memengaruhi niat adopsi GAI untuk pemrograman. Penelitian juga meneliti hubungan ini berdasarkan gender dan usia melalui analisis multigrup. Hasilnya memberikan saran bagi pengembang GAI untuk meningkatkan kualitas kode output dan sistem, yang terbukti memengaruhi persepsi pengguna tentang kegunaan dan kemudahan penggunaan GAI

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Generative Artificial Intelligence (GAI) has become significant in various fields, including as a programming aid in Indonesia. However, research on the adoption of GAI as a programming tool remains limited. This study aims to analyze the factors influencing employees in Indonesia to adopt GAI for programming, focusing on output code quality and system quality, which affect the perceived usefulness and ease of use of GAI. The study employs the PLS-SEM method for quantitative analysis with 497 valid data points and qualitative analysis through interviews with 10 informants. The results indicate that perceived usefulness is influenced by factors such as presentation, structure, interactivity, responsiveness, understandability, assurance, and reliability, while perceived ease of use is influenced by presentation, structure, responsiveness, assurance, and reliability. Both perceptions affect the intention to adopt GAI for programming. The study also examines these relationships based on gender and age using multigroup analysis. The findings provide practical suggestions for GAI developers to enhance the quality of output code and system, which significantly influence users' perceptions of the usefulness and ease of use of GAI."

"Dunia saat ini berkembang pesat dengan kemampuan kecerdasan buatan (AI) untuk membuka cara-cara baru dan menarik untuk melibatkan pelanggan. Dengan memberikan penawaran inovatif dan pengalaman yang dipersonalisasi, AI memperkuat ikatan antara merek dan konsumen mereka, sehingga membedakan mereka dari para pesaing. Berdasarkan model Stimulus-Organism-Response (SOR), penelitian ini bertujuan untuk menganalisis bagaimana faktor stimulus dalam upaya pemasaran Artificial Intelligence (AI) berdampak pada perilaku loyalitas konsumen pada aplikasi layanan pesan antar makanan. Penelitian ini menggunakan pendekatan kuantitatif, deskriptif, dan survei cross-sectional. Structural Equation Model (SEM) digunakan untuk menganalisis 412 tanggapan dari survei kuesioner terhadap subjek Generasi Y dan Z yang menggunakan aplikasi pesan-antar makanan dari 2 platform terkemuka di industri ini, yaitu Gojek (GoFood) dan Grab (GrabFood). Hasil penelitian menunjukkan bahwa semua faktor stimulus dalam upaya pemasaran AI memengaruhi pengalaman merek, sementara hanya informasi dan interaksi yang memengaruhi ekuitas merek. Pengalaman dan ekuitas merek secara signifikan mempengaruhi tanggapan (preferensi merek dan niat penggunaan ulang). Implikasi dari penelitian ini dapat memungkinkan akademisi dan praktisi bisnis untuk memahami pengaruh AI terhadap pengalaman pengguna dan memberikan panduan untuk pengembangan strategi pemasaran dan branding untuk mengupayakan kepuasan pelanggan dengan menawarkan layanan online.

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Today's world thrives on artificial intelligence’s (AI) ability to unlock new and exciting ways to engage customers. By powering innovative offerings and personalized experiences, AI strengthens the bond between brands and their consumers, setting them apart from the competition. According to the Stimulus–Organism–Response (SOR) model, this study aims to analyze how stimulus factors in Artificial Intelligence (AI) marketing efforts impact consumer loyalty behavior in food delivery service applications. This research uses a quantitative, descriptive, and cross-sectional survey approach. Structural Equation Model (SEM) was used to analyze 412 responses from a questionnaire survey of Generation Y and Z subjects who used food delivery service applications from 2 leading platforms in the industry, such as Gojek (GoFood) and Grab (GrabFood). The results showed that all stimulus factors in AI marketing efforts affect brand experience, while only information and interaction affect brand equity. Both brand experience and equity significantly influence responses (brand preference and reuse intention).  Implications of this study can activate academia and business practitioners to understand the influence of AI on user experiences and provide a guide for the development of marketing and branding strategies to strive for customer satisfaction by offering online service."

"With success of ICEEE 2010 in Wuhan, China, and December 4 to 5, 2010, the second International Conference of Electrical and Electronics Engineering (ICEEE 2011) will be held in Macau, China, and December 1 to 2, 2011. This year ICEEE is sponsored by International Industrial Electronics Center, Hong Kong. This book deals with this conference."

"Practitioners in apparel manufacturing and retailing enterprises in the fashion industry, ranging from senior to front line management, constantly face complex and critical decisions. There has been growing interest in the use of artificial intelligence (AI) techniques to enhance this process, and a number of AI techniques have already been successfully applied to apparel production and retailing. Optimizing decision making in the apparel supply chain using artificial intelligence (AI): From production to retail provides detailed coverage of these techniques, outlining how they are used to assist decision makers in tackling key supply chain problems. Key decision points in the apparel supply chain and the fundamentals of artificial intelligence techniques are the focus of the opening chapters, before the book proceeds to discuss the use of neural networks, genetic algorithms, fuzzy set theory and extreme learning machines for intelligent sales forecasting and intelligent product cross-selling systems."

"2012 International Conference on Environment Science and 2012 International Conference on Computer Science (ICES 2012/ICCS 2012) will be held in Australia, Melbourne, 15‐16 March, 2012.Volume 2 contains some topics in intelligent system. There are 51 papers were selected as the regular paper in this volume. It contains the latest developments and reflects the experience of many researchers working in different environments (universities, research centers or even industries), publishing new theories and solving new technological problems.The purpose of volume 2 is interconnection of diverse scientific fields, the cultivation of every possible scientific collaboration, the exchange of views and the promotion of new research targets as well as the further dissemination, the diffusion of intelligent system, including but not limited to intelligent system, neural networks, machine learning, multimedia system and applications, speech processing, image & video signal processing and computer‐aided network design the dispersion. "

"This volume constitutes the thoroughly refereed conference proceedings of the 25th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2012, held in Dalian, China, in June 2012. The total of 82 papers selected for the proceedings were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on machine learning methods, cyber-physical system for intelligent transportation applications, AI applications, evolutionary algorithms, combinatorial optimization, modeling and support of cognitive and affective human processes, natural language processing and its applications, social network and its applications, mission-critical applications and case studies of intelligent systems, AI methods, sentiment analysis for asian languages, aspects on cognitive computing and intelligent interaction, spatio-temporal datamining, structured learning and their applications; decision making and knowledge based systems, pattern recognition, agent based systems, decision making techniques and innovative knowledge management, and machine learning applications."

"This book constitutes the thoroughly refereed post-conference proceedings of the International IFIP WG 5.7 Conference on Advances in Production Management Systems, APMS 2011, held in Stavanger, Norway, in September 2011. The 66 revised and extended full papers were carefully reviewed and selected from 124 papers presented at the conference. The papers are organized in 3 parts: production process, supply chain management, and strategy. They represent the breadth and complexity of topics in operations management, ranging from optimization and use of technology, management of organizations and networks, to sustainable production and globalization. The authors use a broad range of methodological approaches spanning from grounded theory and qualitative methods, via a broad set of statistical methods to modeling and simulation techniques."

"The two LNAI volumes 7208 and 7209 constitute the proceedings of the 7th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2012, held in Salamanca, Spain, in March 2012. The 118 papers published in these proceedings were carefully reviewed and selected from 293 submissions. They are organized in topical sessions on agents and multi agents systems, HAIS applications, cluster analysis, data mining and knowledge discovery, evolutionary computation, learning algorithms, systems, man, and cybernetics by HAIS workshop, methods of classifier fusion, HAIS for computer security (HAISFCS), data mining: data preparation and analysis, hybrid artificial intelligence systems in management of production systems, hybrid artificial intelligent systems for ordinal regression, hybrid metaheuristics for combinatorial optimization and modelling complex systems, hybrid computational intelligence and lattice computing for image and signal processing and nonstationary models of pattern recognition and classifier combinations."