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Abstrak :
Makalah Non-Seminar ini menggali dampak transformatif analitika bisnis dalam organisasi saat ini. Analitika bisnis melibatkan pemeriksaan menyeluruh terhadap data bisnis, mengintegrasikan wawasan dari analisis ke dalam pengambilan keputusan dan perencanaan strategis. Yang dulunya dianggap sebagai produk samping era informasi, data kini telah berkembang menjadi sumber daya berharga, mendorong inovasi dan keunggulan kompetitif. Tantangan dalam menggali wawasan bermakna dari data diakui. Studi ini menilai artikel-artikel penting seperti "The Analytics Mandate," "Big Data: The Management Revolution," "Business Analytics: Why Now and What Next?," "Building the AI-Powered Organization," dan "Analytics as a Source of Business Innovation." Secara kolektif, artikel-artikel ini menekankan pentingnya analitika pada abad ke-21, menyoroti perannya dalam memanfaatkan sumber daya data yang melimpah, mempromosikan pengambilan keputusan berbasis data, dan beradaptasi dengan lingkungan bisnis yang dinamis. Artikel-artikel yang ditinjau memiliki interpretasi umum tentang analitika sebagai penggunaan data untuk perencanaan berbasis fakta, pengambilan keputusan, dan pembelajaran. Mereka menekankan permintaan yang meningkat terhadap analitika sebagai sarana untuk menjaga keunggulan kompetitif. Perusahaan yang mengadopsi pengambilan keputusan berbasis data terbukti lebih unggul dari pesaingnya, menghasilkan peningkatan produktivitas dan profitabilitas. Contoh nyata dari Sears Holdings dan Nedbank disajikan untuk mengilustrasikan bagaimana analitika meningkatkan efisiensi dan ketepatan pemasaran. Diskusi juga mengeksplor temuan survei dalam artikel "Analytics as a Source of Business Innovation," yang mengungkap keuntungan kompetitif yang meningkat, peningkatan inovasi, peran tata kelola data, dan peluang yang diciptakan oleh mesin pintar. Bagian diskusi kritis mengevaluasi efektivitas artikel-artikel tersebut, memuji penggunaan metafora, kejelasan, dan aplikasi dunia nyata. Ini juga menyoroti istilah-istilah yang mungkin memerlukan penjelasan bagi pembaca. Pengenalan Tiga Tingkatan Kematangan Analitika—Inovator Analitik, Praktisi Analitik, dan Tantangan Analitik—mencategorikan perusahaan berdasarkan tingkat kecanggihan analitika. Implikasi untuk praktik menekankan tahap awal pengadopsian analitika, menyajikan banyak peluang untuk pertumbuhan pendapatan, pengurangan biaya, dan manajemen risiko. Tren dalam praktik organisasi yang berfokus pada data menekankan perlunya memperlakukan data sebagai aset berharga, mempromosikan berbagi data, mengatasi interpretasi yang beragam, dan berbeda melalui analitika. Sebagai kesimpulan, paper ini menegaskan bahwa adopsi luas analitika sedang membentuk praktik organisasi. Keberhasilan perusahaan dalam bidang ini tidak hanya bergantung pada pengenalan potensi analitika tetapi juga pada pembentukan budaya analitis, penerimaan pemikiran inovatif, dan transformasi praktik bisnis. Munculnya analitika bisnis diakui sebagai revolusi yang tidak dapat diabaikan yang sangat penting untuk menjaga daya saing dalam lanskap bisnis kontemporer. ......This paper dives into the transformative impact of business analytics in today's organizations. Business analytics entails a thorough examination of business data, integrating insights from the analysis into decision-making and strategic planning. Once considered a byproduct of the information era, data has now evolved into a valuable resource, fueling innovation and competitive advantage. The challenges of extracting meaningful insights from data are acknowledged. The study assesses pivotal articles such as "The Analytics Mandate," "Big Data: The Management Revolution," "Business Analytics: Why Now and What Next?," "Building the AI-Powered Organization," and "Analytics as a Source of Business Innovation." Collectively, these articles underscore the increasing importance of analytics in the 21st century, emphasizing its role in utilizing abundant data, promoting data-driven decision-making, and adapting to dynamic business environments. The reviewed articles share a common interpretation of analytics as the use of data for fact-based planning, decision-making, and learning. They highlight the growing demand for analytics as a means to maintain a competitive edge, showcasing that companies embracing data-driven decision-making outperform competitors, leading to improved productivity and profitability. Real-life examples from Sears Holdings and Nedbank are presented to illustrate how analytics enhances efficiency and marketing precision. The discussion also explores survey findings in the "Analytics as a Source of Business Innovation" article, revealing increased competitive advantages, a surge in innovation, the role of data governance, and opportunities created by smart machines. The critical discussion section evaluates the articles' effectiveness, praising their use of metaphors, clarity, and real-world applications. It also points out unfamiliar terms that may need clarification for readers. The introduction of the Three Levels of Analytics Maturity—Analytical Innovators, Analytical Practitioners, and Analytically Challenged—categorizes companies based on their sophistication in analytics. Implications for practice underscore the early stages of analytics adoption, presenting numerous opportunities for revenue growth, cost reduction, and risk management. Trends in data-centric organizational practices stress treating data as a valuable asset, promoting data sharing, addressing diverse interpretations, and achieving differentiation through analytics. In conclusion, the paper asserts that the widespread adoption of analytics is reshaping organizational practices. Success in this field is not solely dependent on recognizing analytics potential but also on fostering an analytical culture, embracing innovative thinking, and transforming business practices. The rise of business analytics is acknowledged as an undeniable revolution crucial for maintaining competitiveness in the contemporary business landscape.

Abstrak :
Ringkasan artikel ini didasarkan pada wawasan dari empat artikel terkait untuk melakukan penyelidikan mendalam terhadap jalur revolusioner analitika dalam dunia berbasis data modern. Artikel 1 memperkenalkan kerangka struktural untuk memanfaatkan data dan menekankan sifat revolusioner serta pentingnya analitika bisnis. Artikel 2 mengkaji nuansa analitika bisnis, termasuk definisi bidang ini, signifikansi yang meningkat, evolusi historis, dan hambatan pengadopsian. Kekuatan revolusioner analitika bisnis dalam pengambilan keputusan dan pergerakan akademis menuju program analitika ditekankan dalam Artikel 3, yang juga menyoroti signifikansi yang semakin meningkat dari analitika bisnis baik dalam sektor bisnis maupun akademis. Terakhir, Artikel 4 menjelajahi manfaat dan kerugian yang mungkin terjadi dalam analitika Big Data di industri minyak dan gas. Artikel-artikel ini memberikan pandangan 360 derajat tentang analitika, berfokus pada interdisiplineritas bidang ini dan peran sentralnya dalam menentukan lanskap komersial dan akademis kontemporer. Artikel ini merangkum temuan-temuan tersebut, menyoroti potensi permainan yang dapat mengubah analitika sambil juga mengakui banyaknya hambatan dan imbalan yang menanti bagi mereka yang memilih untuk menggunakan data dan analitika. Manfaat potensial dari penggunaan analitika bisnis dan Big Data termasuk peningkatan pengambilan keputusan, operasi yang lebih efisien, dan fokus yang lebih besar pada pelanggan. Mereka juga berfungsi sebagai dorongan kreativitas, memungkinkan perusahaan untuk mengantisipasi dan mempersiapkan diri terhadap bahaya dan peluang di masa depan. Dunia akademis juga berkembang, dengan kursus dan program baru yang diperkenalkan di universitas untuk membantu mahasiswa mengembangkan kemampuan analitis yang dicari oleh para pengusaha. Meskipun perkembangan dan penggunaan teknologi baru yang mengesankan, masalah seperti integritas dan privasi data tetap menjadi masalah di sektor ini. Meskipun demikian, analitika bisnis sangat penting di dunia akademis dan korporat karena sifatnya yang multiaspek, rentang aplikasinya yang luas, dan kemampuannya untuk merevolusi seluruh industri. ......This article review draws on the insights of four related articles to undertake an in-depth investigation of the revolutionary path of analytics in the modern data-driven world. Article 1 introduces a structural framework for making use of data and stresses the revolutionary nature and critical importance of business analytics. Article 2 examines business analytics' nuances, including the field's definition, rising significance, historical evolution, and obstacles to adoption. The revolutionary power of business analytics in decision-making and the academic movement towards analytics programmes are emphasised in Article 3, which also highlights the increasing significance of business analytics in both the business sector and academia. Finally, Article 4 delves into Big Data analytics’ possible benefits and drawbacks in the oil and gas business. These articles provide a 360-degree view of analytics, focusing on the field's interdisciplinarity and its central role in defining contemporary commercial and academic landscapes. This article summarises these findings, highlighting the game-changing potential of analytics while also recognising the many obstacles and rewards that lie ahead for those who choose to use data and analytics. The potential benefits of using business analytics and Big Data include improved decision-making, more efficient operations, and a greater focus on customers. They also operate as impetuses for creativity, letting firms anticipate and prepare for future dangers and opportunities. The academic world is also evolving, with new courses and programmes introduced at universities to help students develop the analytical abilities employers seek. Despite the impressive development and use of new technologies, issues like data integrity and privacy remain a problem in the sector. Despite this, business analytics is of the utmost importance in academia and the corporate world because of its multifaceted character, wide range of applications, and ability to revolutionise entire industries.

Abstrak :
Many important planning decisions in society and business depend on proper knowledge and a correct understanding of movement, be it in transportation, logistics, biology, or the life sciences. Today the widespread use of mobile phones and technologies like GPS and RFID provides an immense amount of data on location and movement. What is needed are new methods of visualization and algorithmic data analysis that are tightly integrated and complement each other to allow end-users and analysts to extract useful knowledge from these extremely large data volumes. This is exactly the topic of this

Abstrak :
Expanding the frontiers of visual analytics and visualization provides a review of the state of the art in computer graphics, visualization, and visual analytics by researchers and developers who are closely involved in pioneering the latest advances in the field. It is a unique presentation of multi-disciplinary aspects in visualization and visual analytics, architecture and displays, augmented reality, the use of color, user interfaces and cognitive aspects, and technology transfer. It provides readers with insights into the latest developments in areas such as new displays and new display processors, new collaboration technologies, the role of visual, multimedia, and multimodal user interfaces, visual analysis at extreme scale, and adaptive visualization.

Abstrak :
Fokus di dalam penelitian ini adalah Implementasi Kebijakan Sistern Pelatihan Kerja Nasional pada Balai Latihan Kerja Unit Pelaksana Teknis Pusat Direktorat Jenderal Pernbinaan Pelatihan dan Produktivitas.. Penelitian ini menggunakan teori implementasi kebijakan yang dikemukakan oleh Edward George III. dalam bukunya yang beljudul Implementing Public Policy. Menurut Edward George III suatu kebijakan dapat dinilai implement/asinya dengan mengacu pada empat variabel yang terkait satu sama lain. Keempat variabel tesebut adalah vaxiabel komunikasi (dengan indikator : penyaluran, kejelasan dan konsistensi), variabel sumber daya (sumber daya manusia, kewenangan, informasi dan sarana dan parasana), variabel sikap dan variabel struktur birokrasi (slandar operation prosedur dan iiagmentasi). Populasi penelitian ini adalah ll Kepala Balai latihan Kexja Unit pelaksana Teknis Pusat.. Data yang digunakan adalah data primer berupa kuesioner dengan menggunakan skala Likert untuk dalam metodenya dan wawancara dengan informan yan mengetahui tcntang Sistem Pelatihan Kelja Nasional. Sedangkan data sckunder, berupa literatur, buku, artikel, perundang-undangan dan dokumen yang terkait dengan penclitian. Berdasarkan pengolahan data penelitian, maka dapat disimpulkan bahwa impementasi kebijakan pembinaan Sistem Pelatihan Kexja Nasional (Sislatkernas) pada Balai Latihan Kelja Unit Pelaksana Teknis Pusat (BLK-UPTP) di lingkungan Direktorat Jenderal Pembinaan Pelatihan dan Produktivitas sudah beljalan dengan baik. Ini di dapat dengan melihat variabel komunikasi memperoleh skor relatif 77,I7%, variabel sumber daya mendapatkan skor relatif 72,87%, variabel sikap dengan skor relatif 74,55% dan variabcl struktur birokrasi dengan skor nelatif 73,94%. dapat dapat di golongkan baik. Rckapitulasi dari skor relatif variabel-variabel diatas menunjuklcan skor relatif 74,63% schingga berdasarkan acuan interpretasidengan skor tesebut dapat digolongkan baik. Implementasi kebijakan pcmbinaan Sislatkemas pada BLK-UPTP dapat berjalan lebih baik lagi maka Ditjen Binalattas perlu mcngadakan pemetaan tentang kualifrkasi instruktur dan mengupayakan peningkatan kualifikasi inslruktur melalui diklat, sosialisasi, bimbingan tcknis, workshop serta uji kompetensi bagi instruktur dan penyediaan anggaran untuk menjadikan kcjuruan-kejuruan yang ada di BLK-UPTP dapat menjadi Tempat Uji Kompetensi. ......The focus in this research is Policy’s Implementation of National Working Training System {Sislatkemas) in Vocational Training Centre on Centre Technical Implementer Unit of Directorate General of Development of Training and Productivity. This research uses Edward George ill policy implementation theory of his book "Implementing Public Policy". According to Edward George Ill, as policy can be assessed the its implementation with the connection of 4 variables. 4 variables are : communication variable (the indicator : distribution, clarity, and consistency), resources variable (human resources, authority, information, and facilities and infrastructure), attitude variable and birocracy structure variable (standard operation procedure and iragrnentation). Population in this research is ll head of Vocational Training Centre on Centre Technical Implementer Unit. Primary data is likert questionnaire and interview to knowing of National Working Training System (Sislatkemas). As secondary data are literatures, books, articles, legislation, and the other documents which related with this research. Based of research data processing, it can be concluded; the development policy .implementation of National Working Training System (Sislatkemas) at Vocational Training Centre on Centre Technical lmplernenter Unit of Directorate General of Development of Training and Productivity is mmiing well. It can be seen the communication variable has score 77,l7%, resources variable has score 72,87%, attitude variable has score 74,5S% and birocracy structure variable has score 73,94%. This score can be classified as good. Recapitulation of variable relative score shown relative score 74,63%. This score, based on reference interpretation can be classitied as good. Policy implementation of National Working Training System (Sislatkemas) at Vocational Training Centre on Centre Technical lmplementer Unit can be running better, so Directorate General of Development of Training and Productivity needs to do a mapping about instructor qualilication and see about instructor qualification development through training, socialization, technical guidance, workshop, and competencies test to instructor and budgeting provision to make vocation on Vocational Training Centre on Centre Technical Implementer Unit can be the place of competency test.

Abstrak :
Pesatnya perkembangan proses audit menggunakan pendekatan data analytics menawarkan manfaat kompetitif yang signifikan bagi organisasi yang dapat memanfaatkan lingkungan berbasis data, termasuk kantor akuntan publik. Namun pada saat ini belum banyak kantor akuntan publik yang memanfaatkan data analytics. Penelitian ini bertujuan untuk mengevaluasi kesiapan implementasi data analytics dalam audit pada KAP DNA, salah satu kantor akuntan publik terbesar di dunia yang berafiliasi internasional. KAP DNA telah menginisiasi penggunaan data analytics mulai dari tahun 2016. Proses penelitian ini dilakukan dengan metode kualitatif dimana narasumber dipilih berdasarkan snowball sampling sejumlah 5 narasumber yang terdiri dari manajer senior, manajer, dan senior auditor. Alat ukur evaluasi kesiapan menggunakan kriteria Acatech indeks maturitas industri milik Schuh et al. (2017) yang telah dikembangkan di penelitian Gürdür et al. (2019). Kriteria tersebut terdiri dari kesiapan sumber daya, kesiapan sistem informasi, kesiapan budaya, dan kesiapan organisasi. Berdasarkan hasil penelitian, KAP DNA memiliki tingkat kesiapan menengah secara umum. Hal ini disebabkan oleh tantangan seperti kurangnya kemampuan auditor, kekurangan sumber daya manusia tim khusus data analytics, dan perangkat yang belum memadai. KAP DNA direkomendasikan untuk mempertimbangkan menambah pelatihan terkait data analytics kepada auditor di saat low season, merekrut karyawan tambahan di tim khusus data analytics, dan menyediakan perangkat yang memadai. ......The rapid development of the audit process using a data analytics approach offers significant competitive advantages for organizations that can take advantage of a data-driven environment, including public accounting firms. However, currently not many public accounting firms are utilizing data analytics. This study aims to evaluate the readiness to implement data analytics in audits at KAP DNA, one of the world's largest public accounting firms with international affiliations. KAP DNA has initiated the use of data analytics starting in 2016. This research process was carried out using a qualitative method where the informants were selected based on snowball sampling of 5 sources consisting of senior managers, managers, and senior auditors. The measuring instrument for readiness evaluation uses the Acatech industrial maturity index criteria belonging to Schuh et al. (2017) which has been developed in the research of Gürdür et al. (2019). The criteria consist of resource readiness, information system readiness, cultural readiness, and organizational readiness. Based on the research results, KAP DNA has a medium level of readiness in general. This is due to challenges such as lack of auditor capabilities, lack of human resources for dedicated data analytics teams, and inadequate tools. KAP DNA is recommended to consider adding training related to data analytics to auditors during low season, recruiting additional employees in a dedicated data analytics team, and providing adequate tools.

Abstrak :
This textbook on practical data analytics unites fundamental principles, algorithms, and data. Algorithms are the keystone of data analytics and the focal point of this textbook. Clear and intuitive explanations of the mathematical and statistical foundations make the algorithms transparent. But practical data analytics requires more than just the foundations. Problems and data are enormously variable and only the most elementary of algorithms can be used without modification. Programming fluency and experience with real and challenging data is indispensable and so the reader is immersed in Python and R and real data analysis. By the end of the book, the reader will have gained the ability to adapt algorithms to new problems and carry out innovative analyses. This book has three parts: (a) Data Reduction: Begins with the concepts of data reduction, data maps, and information extraction. The second chapter introduces associative statistics, the mathematical foundation of scalable algorithms and distributed computing. Practical aspects of distributed computing is the subject of the Hadoop and MapReduce chapter. (b) Extracting Information from Data: Linear regression and data visualization are the principal topics of Part II. The authors dedicate a chapter to the critical domain of Healthcare Analytics for an extended example of practical data analytics. The algorithms and analytics will be of much interest to practitioners interested in utilizing the large and unwieldly data sets of the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System. (c) Predictive Analytics Two foundational and widely used algorithms, k-nearest neighbors and naive Bayes, are developed in detail. A chapter is dedicated to forecasting. The last chapter focuses on streaming data and uses publicly accessible data streams originating from the Twitter API and the NASDAQ stock market in the tutorials. This book is intended for a one- or two-semester course in data analytics for upper-division undergraduate and graduate students in mathematics, statistics, and computer science. The prerequisites are kept low, and students with one or two courses in probability or statistics, an exposure to vectors and matrices, and a programming course will have no difficulty. The core material of every chapter is accessible to all with these prerequisites. The chapters often expand at the close with innovations of interest to practitioners of data science. Each chapter includes exercises of varying levels of difficulty. The text is eminently suitable for self-study and an exceptional resource for practitioners.

Abstrak :
Karakteristik dari transformasi digital pada manufaktur adalah penerapan teknologi mutakhir yang mendukung proses dan informasi yang terkoneksi antara mesin-mesin produksi, dan produk serta adaptabilitas tinggi dari suatu sistem produksi. Dalam mencapai tujuan utama Industri 4.0 yaitu smart manufacturing yang dapat merespons fluktuasi permintaan pasar terhadap produk berkualitas tinggi, dibutuhkan penerapan teknologi yang dapat mengumpulkan dan menganalisis data yang menghasilkan solusi secara cerdas disebut sebagai pemanfaatan Data Analytics. Tujuan pada penelitian ini adalah memberikan rekomendasi strategi berupa urutan prioritas kriteria kesuksesan untuk dapat digunakan para pemangku kepentingan di perusahaan manufaktur dengan melakukan implementasi transformasi digital yang efektif. Metode AHP (Analytic Hierarchy Process) digunakan pada penelitian ini untuk mendapatkan prioritas faktor kesuksesan yang menjadi dasar rekomendasi strategi dalam meningkatkan efektivitas implementasi Data Analytics pada manufaktur. Hasil penelitian ditemukan bahwa 3 faktor kesuksesan teratas adalah Effective data driven communication (People & Management), Technology & Infrastructure Integration (Technology) kemudian Training & Upskilling (People & Management). ......Technological developments always create new challenges for organizations to adapt so that they remain competitive. Today, organizations are dealing with rapid technological developments and the disruption of digital transformation. The characteristics of digital transformation in manufacturing are the application of the latest technology that supports so that processes and information are connected, between production machines, and products as well as the high adaptability of a production system. In achieving the main goal of Industry 4.0, namely smart manufacturing that can respond to fluctuations of market demand for high-quality products, it is necessary to apply technology that can collect and analyze data to produce intelligent solutions, which is often referred to as the use of Data Analytics. Literature study shows that there are various barriers or barriers in the implementation of Data Analytics in manufacturing companies. However, none of these studies have discussed what success factors need to be prioritized for treatment. This causes the implementation of Data Analytics in manufacturing to be less effective. The aim of this research is to provide strategic recommendations in the form of a priority sequence of success criteria that can be used by stakeholders in manufacturing companies to be able to implement effective digital transformation. Determining the priority of handling obstacles in the implementation of Data Analytics is a Multi-criteria Decision Making (MCDM) problem, the AHP method is used in this study to obtain priority success factors which are the basis for strategic recommendations in increasing the effectiveness of Data Analytics implementation in manufacturing. From the research results, it was found that the top 3 success factors were Effective data driven communication (People & Management), Technology & Infrastructure Integration (Technology) then Training & Upskilling (People & Management).

Abstrak :
Perkembangan era big data telah mendorong industri untuk mengadopsi teknologi data analitik untuk kebutuhan internal audit bekerja lebih efisien dan efektif. Penelitian ini menginvestigasi faktor-faktor yang mempengaruhi adopsi teknologi data analytics untuk audit internal oleh sebuah perusahaan swasta. Kerangka teoritis studi berlandaskan pada Technology, Organization, and Environment (TOE) yang mengelaborasi komponen lingkungan dalam membahas faktor-faktor yang mendorong pengadopsian sebuah teknologi. Dengan metode studi kasus dan menggunakan in-depth interview untuk pengumpulan data primer di samping data sekunder, penelitian ini melakukan wawancara kepada 4 partisipan yang bekerja sebagai auditor internal di PT. XXX. Hasil temuan mengungkapkan bahwa faktor yang mempengaruhi adopsi data analytics di PT. XXX dari konteks teknologi ditentukan adalah relatif advantage dan complexity, sementara konteks organisasi ditentukan oleh quality of human resources dan top management support. Pada prakteknya, pemanfaatan data analytics di PT. XXX adalah untuk mencari anomali dalam populasi audit, menerapkan audit berkelanjutan (continuous auditing) serta analisa data audit dengan merekam lebih dari 1 juta data. Penggunaan data analytics untuk kegiatan audit masih cukup terbatas sehingga regulator diharapkan dapat membentuk standar audit dan mengadakan training untuk meningkatkan keterampilan auditor dalam penggunaan data analytics. Para akademisi juga diharapkan dapat menyusun dan membentuk kurikulum praktik untuk mempersiapkan para lulusannya berkiprah di dalam penggunaan data analytics. ......The big data era has driven industry to adopt data analytics technology, particularly in the field of internal audit. This study systematically investigates into factors that influence the adoption of data analytics technology for internal audit by a private company. Technology, Organization, and Environment (TOE) framework is used to explain the antecedents of the adoption of data analytics technology. Using a case study design and in-depth interviews for primary data collection, the findings reveal that technological and organizational contexts are the major drivers of technology adoption in data analytics. In practice, the use of data analytics at PT. XXX is to identify the presence of anomalies in the audit population, implement continuous auditing and analyze audit data by using more than 1 million data. The use of data analytics for audit activities is still found limited, therefore regulators are advised to set reformed audit standards and provide training to enhance the use of data analytics. In addition, the role of academics to develop hands-on curriculum for students to develop their skills in data analytics is deemed crucial in promoting the advancement of data analytics technology for internal audit work.