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"This book teaches you, based on the open-source programming language R, how to read, process, and analyze data from various formats and sources, and how to use Artificial Intelligence and Machine Learning in your company. It specifically explains the three types of Machine Learning and selected, widely-used algorithms that form the basis of Artificial Intelligence. Additionally, the author provides you with the datasets used through his R package "machinelearning," so you can run the code examples presented in the book yourself. The package also includes interactive self-learning tutorials for R. Contents Benefits of Machine Learning and Artificial Intelligence Best Practices Fundamentals of the R Programming Language Fundamentals of Machine Learning with R, including preprocessing, exploratory data analysis, modeling, evaluation, and parameter tuning Application of Machine Learning with R for predictions, classification, clustering, and recommendation systems The Author Bernd Heesen is a professor at the Faculty of Economics at Ansbach University of Applied Sciences in Bavaria. Before his tenure at the university, he worked for more than 10 years as a business consultant both domestically and internationally. He continues to advise companies on the use of IT innovations. The translation was done with the help of artificial intelligence. A subsequent human revision was done primarily in terms of content. This book is a translation of an original German edition. The translation was done with the help of artificial intelligence (machine translation by the service DeepL.com). A subsequent human revision was done primarily in terms of content, so that the book will read stylistically differently from a conventional translation"

"The book shows application potentials of artificial intelligence in various industries and presents application scenarios on how a practical implementation can take place. The starting point is the description of legal aspects, which includes a European regulation for artificial intelligence and addresses the question of the permissibility of automated decisions. The description of various application potentials, mostly industry-related, and the presentation of some application scenarios form the focus of the topic volume. The book is based on the question of how artificial intelligence can be used in entrepreneurial practice. It offers important information that is just as relevant for practitioners as for students and teachers. This book is a translation of an original German edition. The translation was done with the help of artificial intelligence (machine translation by the service DeepL.com). A subsequent human revision was done primarily in terms of content, so that the book will read stylistically differently from a conventional translation"

"Gain exclusive access to the secrets to building an enterprise AI start-up. AI innovation helps with every aspect of the business, from the supply chain, marketing, and advertising, customer service, risk management, operations to security. Industries from different verticals have been adopting AI and get real business values out of it. This book guides you through each step, from defining the business need and business model, all the way to registering IP and calculating your AI start-up valuation. You see how to perform market and technology validation, perform lean AI R&D, design AI architecture, AI product development and operationalization. The book also cover building and managing an AI team, along with attracting and keeping business and developer users, Building an Enterprise AI start-up is hard because Enterprise AI is an effort to build applications to mimic human intelligence to solve business problems. Hence it has a different challenge from building traditional non-AI applications, such as scouting, recruiting and managing AI talents; designing the most cost-efficient and scalable Enterprise AI; or establishing the best practice to operationalize AI in production As we are in the dawn of the AI-first product wave, AI-powered products for enterprises will be created for many years to come and AI Startup Strategy is the one-stop guide for it. You will: Match customers expectation VS technical feasibility Justify business values and ROI for customers Review the best business models for high valuation enterprise AI start-ups Design an AI product that gives a satisfactory experience for the user Register and value AI IP"

"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."

"Privasi data menjadi perhatian krusial dalam lanskap bisnis saat ini, terutama dengan Big Data dan Analytics (BD&A) serta kecerdasan buatan (AI). Diulas melalui empat artikel, lanskap analitika bisnis yang terus berkembang membahas aspek sejarah, tantangan implementasi, dan perannya yang transformatif. Sambil menyoroti manfaat BD&A dan AI, esai menekankan kebutuhan mendesak akan kesadaran dan langkah-langkah proaktif untuk mengatasi isu privasi data. Esai ini menekankan dampak negatif dari pengumpulan data yang luas dan menganjurkan perlindungan informasi pribadi melalui regulasi yang ketat. Diskusinya menekankan kesiapan organisasi dan pengembangan kepemimpinan untuk mengatasi tantangan dalam adopsi BD&A sambil memastikan perlindungan data yang sensitif. Esai ini menyimpulkan dengan mengajak untuk lebih mendalami privasi data melalui studi kasus di masa depan untuk mengurangi risiko dalam penanganan informasi rahasia di lingkungan digital yang dinamis.

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Data privacy is a critical concern in today's business landscape, particularly with Big Data and Analytics (BD&A) and artificial intelligence (AI). Explored through four articles, the evolving business analytics landscape addresses historical aspects, implementation challenges, and its transformative role. While highlighting the benefits of BD&A and AI, the essay emphasizes the urgent need for awareness and proactive measures to address data privacy issues. It underscores the drawbacks of extensive data collection and advocates for safeguarding personal information through stringent regulations. The discussion stresses organizational readiness and leadership development to navigate challenges in BD&A adoption while ensuring sensitive data protection. The essay concludes by calling for deeper exploration of data privacy in future case studies to mitigate risks in handling confidential information in the dynamic digital environment."

"ABSTRAKPemberitahuan dokumen impor yang dilakukan secara self-assessment perlu dilakukan penelitian kembali oleh pemeriksa dokumen, dikarenakan ada kemungkinan terjadinya kesalahan pemberitahuan baik yang disengaja maupun tidak disengaja. Meskipun demikian, penelitian kembali belum berjalan dengan optimal. Penelitian ini melakukan pendekatan business intelligence untuk menjawab permasalahan tersebut dengan memberikan kemampuan analisis kepada pemeriksa dokumen. Pendekatan tersebut difokuskan pada pengembangan data warehouse dengan metodologi Kimball. Hasil dari penelitian ini adalah rancangan data warehouse yang dapat dimanfaatkan untuk kebutuhan dashboard, OLAP, dan data mining untuk melakukan pemodelan pemberitahuan dokumen impor dengan menggunakan algoritme decision tree, support vector machine, dan neural network.

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ABSTRACTThe customs declaration that carried out by self-assessment needs to be re-examined by the document examiner. There is a possibility that customs declaration have an error to define even on purpose or not. However, the condition of re-examination by document examiners has not run optimally. This study approached business intelligence to answer these problems by providing analysis capabilities to document examiners. The approach was focused on developing a data warehouse with Kimballs methodology. The result of this study is the design of a data warehouse that can be used for the needs of dashboards, OLAP, and data mining to create a model of customs declaration using several algorithms, such as decision tree, support vector machine, and neural network."