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"Disertasi ini merupakan deskripsi, analisis dan interpretasi atas data dan pendataan perdesaan pasca lahirnya Undang-Undang Nomor 6 Tahun 2014 tentang Desa. Penulis mengusulkan tujuh tujuan penelitian saat ini. Pertama mengungkap kualitas data perdesaan, berupa data birokrat dan data warga yang menjadi basis data kebijakan publik. Kedua, mengungkap kekerasan simbolik pada pendataan perdesaan top down yang berpedoman pada norma yuridis melalui rekonstruksi genesis data birokrat. Ketiga, mendeskripsikan afirmasi simbolik pada pendataan perdesaan bottom up yang berpedoman pada norma sosiologis melalui rekonstruksi genesis data warga. Keempat, memetakan arena dan aktor pada pendataan perdesaan top down dan bottom up, serta relasinya dengan meta kapital perdesaan. Kelima, mengungkap kekerasan simbolik pada pendataan perdesaan top down yang mereproduksi kebijakan rekolonialisasi Keenam, mendeskripsikan dan menganalisis afirmasi simbolik pada pendataan perdesaan bottom up memproduksi kebijakan afirmatif. Ketujuh, menginterpretasikan kebijakan afirmatif sebagai implementasi amanat konstitusi untuk mencapai lima aspek kesejahteraan rakyat. Area studi: Desa Sibandang, Desa Pantai Bakti dan Desa Tegalallang. Penelitian menggunakan Mixed Methods Research (MMR) dengan Nesting Quantitative Data in Qualitative Designs. Data kualitatif diperoleh melalui in-depth interview dan Focus Group Discussion (FGD, diskusi terpumpun). Data kuantitatif dari Kementerian Dalam Negeri dan dari Badan Pusat Statistik, serta data mandiri dari praktik pendataan perdesaan bottom up. Pisau analisisnya menggunakan konsepkonsep Pierre Bourdieu dan Nick Couldry. Hasil penelitian menunjukkan kebijakan rekolonialisasi dan 'the vicious circle' kebijakan rekolonialisasi yang mengonfirmasi terbuktinya hipotesis, yaitu: semakin kuat doxa kekerasan simbolik pada norma yuridis pendataan, semakin kuat pseudo data, semakin kuat pseudo kebijakan publik; semakin kuat pseudo kebijakan publik, semakin kuat pseudo otoritas, semakin buruk perencanaan, pemrograman, penganggaran, pelaksanaan, pemantauan dan pengawasan kebijakan publik, semakin buruk pencapaian lima aspek kesejahteraan rakyat; semakin buruk pencapaian lima aspek kesejahteraan rakyat, perdesaan semakin termarginalkan; semakin kuat doxa kekerasan simbolik norma yuridis mereproduksi pseudo data, semakin berkesinambungan kekerasan simbolik; dan semakin berkesinambungan kekerasan simbolik, semakin dibutuhkan heteredoxa afirmasi simbolik, yang digambarkan dengan antitesa 'the truth circle' kebijakan afirmatif. Sintesa yang diusulkan dari disertasi ini adalah bagaimana membangun sistemik kebijakan publik berdasarkan pendataan desa berbasis pembangunan ilmu pengetahuan dan teknologi, sehingga memungkinkan lebih banyak ruang untuk komunikasi dan partisipasi penduduk desa.

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This dissertation describes, analyzes, and interprets big data within village data collection, following the ratification of Law of the Republic of Indonesia, Number 6 of 2014, concerning Village. The author proposes seven aims of current research. First, to unveil the quality of village data collection composed of bureaucratic data and villagers' data, which serves as the foundation of current public policy. Second, to reveal the symbolic violence found in the top-down model of village data collection, which refers dominantly to the juridical norms, by performing a bureaucratic data genesis reconstruction process. Third, to describe the symbolic affirmation of the bottom-up model of village data collection, which refers to sociological norms, by performing villagers' data genesis reconstruction process. Fourth, to design a map of the arena and actors involved in both models of village data collection, top-down and bottom-up, by relating them with a metacapital of the Village. Fifth, to expose the symbolic violence found in the top-down model of village data collection, which reproduces recolonization policy. Sixth, to describe and analyze the symbolic affirmation of the bottom-up model of village data collection, which produces affirmative policy. Seventh, to interpret the affirmative policy perceived as the implementation of the Constitutional mandate to finally achieve five dimensions of people's welfare. The research area comprises three distinct villages: Sibandang village in North Sumatera, Pantai Bakti village in West Java, and Tegallalang village in Bali. The author employs Mixed Methods Research (MMR) with Nesting Quantitative Data in Qualitative Designs. Qualitative data was obtained through in-depth interviews and Focus Group Discussions. Quantitative data was obtained from The Ministry of Internal Affairs and the Central Bureau of Statistics (BPS), supporting data from the researcher's independent enterprise and the bottom-up village data collection practices. The data was analyzed using conceptual tools from Pierre Bourdieu and Nick Couldry. The research findings show that recolonization policy and the vicious circle of derivative rules confirm the following hypotheses: the stronger symbolic violence doxa on the juridical norms of village data collection, the stronger pseudo data becomes and the stronger grips of pseudo-public policy; the stronger pseudo-public policy exists, the stronger pseudo authority exercises power, the worse planning, programming, budgeting, implementation, monitoring and surveillance of public policy becomes, and the further to achieve the five dimensions of people's welfare; the worse achievement of the five dimensions of people's welfare, the more marginalized villages become; the stronger symbolic violence doxa on the juridical norms reproduces pseudo data, the more sustainable symbolic violence becomes; and the more sustainable of symbolic violence, the more heteredoxa of symbolic affirmation needed—portrayed as the antithesis of 'the truth circle' of affirmative policy. The synthesis proposed from this dissertation would be how to build the systemic public policies based on the constructed version of science and technology's village data collection, allowing more space for villagers' communication and participation. "

"Perkembangan teknologi informasi dan internet dalam berbagai sektor kehidupan menyebabkan terjadinya peningkatan pertumbuhan data di dunia. Pertumbuhan data yang berjumlah besar ini memunculkan istilah baru yaitu Big Data. Karakteristik yang membedakan Big Data dengan data konvensional biasa adalah bahwa Big Data memiliki karakteristik volume, velocity, variety, value, dan veracity. Kehadiran Big Data dimanfaatkan oleh berbagai pihak melalui Big Data Analytics, contohnya Pelaku Usaha untuk meningkatkan kegiatan usahanya dalam hal memberikan insight yang lebih luas dan dalam. Namun potensi yang diberikan oleh Big Data ini juga memiliki risiko penggunaan yaitu pelanggaran privasi dan data pribadi seseorang. Risiko ini tercermin dari kasus penyalahgunaan data pribadi Pengguna Facebook oleh Cambridge Analytica yang berkaitan dengan 87 juta data Pengguna. Oleh karena itu perlu diketahui ketentuan perlindungan privasi dan data pribadi di Indonesia dan yang diatur dalam General Data Protection Regulation (GDPR) dan diaplikasikan dalam Big Data Analytics, serta penyelesaian kasus Cambridge Analytica-Facebook. Penelitian ini menggunakan metode yuridis normatif yang bersumber dari studi kepustakaan. Dalam Penelitian ini ditemukan bahwa perlindungan privasi dan data pribadi di Indonesia masih bersifat parsial dan sektoral berbeda dengan GDPR yang telah mengatur secara khusus dalam satu ketentuan. Big Data Analytics juga memiliki beberapa implikasi dengan prinsip perlindungan privasi dan data pribadi yang berlaku. Indonesia disarankan untuk segera mengesahkan ketentuan perlindungan privasi dan data pribadi khusus yang sampai saat ini masih berupa rancangan undang-undang.

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The development of information technology and the internet in various sectors of life has led to an increase in data growth in the world. This huge amount of data growth gave rise to a new term, Big Data. The characteristic that distinguishes Big Data from conventional data is that Big Data has the characteristic of volume, velocity, variety, value, and veracity. The presence of Big Data is utilized by various parties through Big Data Analytics, for example for Corporation to incurease their business activities in terms of providing broader and deeper insight. But this potential provided by Big Data also comes with risks, which is violation of one's privacy and personal data. One of the most scandalous case of abuse of personal data is Cambridge Analytica-Facebook relating to 87 millions user data. Therefor it is necessary to know the provisions of privacy and personal data protection in Indonesia and which are regulated in the General Data Protection (GDPR) and how it applied in Big Data Analytics, as well as the settlement of the Cambridge Analytica-Facebook case. This study uses normative juridical methods sourced from library studies. In this study, it was found that the protection of privacy and personal data in Indonesia is still partial and sectoral which is different from GDPR that has specifically regulated in one bill. Big Data Analytics also has several implications with applicable privacy and personal data protection principles. Indonesia is advised to immediately ratify the provisions on protection of privacy and personal data which is now is still in the form of a RUU."

"This book highlights the state of the art and recent advances in Big Data clustering methods and their innovative applications in contemporary AI-driven systems. The book chapters discuss Deep Learning for Clustering, Blockchain data clustering, Cybersecurity applications such as insider threat detection, scalable distributed clustering methods for massive volumes of data; clustering Big Data Streams such as streams generated by the confluence of Internet of Things, digital and mobile health, human-robot interaction, and social networks; Spark-based Big Data clustering using Particle Swarm Optimization; and Tensor-based clustering for Web graphs, sensor streams, and social networks. The chapters in the book include a balanced coverage of big data clustering theory, methods, tools, frameworks, applications, representation, visualization, and clustering validation. "

"Saat ini kompetisi di industri telekomunikasi semakin ketat. Perusahaan telekomunikasi yang dapat tetap menghasilkan banyak keuntungan yaitu perusahaan yang mampu menarik dan mempertahankan pelanggan di pasar yang sangat kompetitif dan semakin jenuh. Hal ini menyebabkan perubahan strategi banyak perusahaan telekomunikasi dari strategi 'growth '(ekspansi) menjadi 'value added services'. Oleh karena itu, program mempertahankan pelanggan ('customer retention') saat ini menjadi bagian penting dari strategi perusahaan telekomunikasi. Program tersebut diharapkan dapat menekan 'churn' 'rate 'atau tingkat perpindahan pelanggan ke layanan/produk yang disediakan oleh perusahaan kompetitor. Program mempertahankan pelanggan ('customer retention') tersebut tentunya juga diimplementasikan oleh PT Telekomunikasi Indonesia, Tbk (Telkom) sebagai perusahaan telekomunikasi terbesar di Indonesia. Program tersebut diterapkan pada berbagai produk Telkom, salah satunya Indihome yang merupakan 'home services' berbasis 'subscriber' berupa layanan internet, telepon, dan TV interaktif. Melalui kajian ini, penulis akan menganalisa penyebab 'churn' pelanggan potensial produk Indihome tersebut, sehingga Telkom dapat meminimalisir angka 'churn' dengan melakukan program 'customer retention' melalui 'caring' yang tepat. Mengingat ukuran 'database' pelanggan Indihome yang sangat besar, penulis akan menganalisis data pelanggan tersebut menggunakan metoda 'Big Data Analytics'. 'Big Data' merupakan salah satu metode pengelolaan data yang sangat besar dengan pemetaan dan 'processing' data. Melalui berbagai bentuk 'output', implementasi 'big data' pada perusahaan akan memberikan 'value' yang lebih baik dalam pengambilan keputusan berbasis data.

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Nowadays, telecommunication industry is very competitive. Telecommunication companies that can make a lot of profit is the one who can attract and retain customers in this highly competitive and increasingly saturated market. This causes change of the strategy of telecommunication companies from growth strategy toward value added services. Therefore, customer retention program is becoming very important in telecommunication companies strategy. This program hopefully can reduce churn rate or loss of potential customers due to the shift of customers to other similar products.

Customer retention program also implemented by PT Telekomunikasi Indonesia, Tbk (Telkom) as the leading telecommunication company in Indonesia. Customer retention program implemented for many Telkom products, including Indihome, a home services based on subscriber which provide internet, phone, and interactive TV. Through this study, the authors will analyze the cause of churn potential customers Indihome product, so that Telkom can minimize the churn number by doing customer retention program through the efficient caring.

Given by huge customer database the author will analyze using Big Data analytics method. Big Data is one method in data management that contain huge data, by mapping and data processing. Through various forms of output, big data implementation on the organization will provide better value in data-based decision making."

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ABSTRACTBig Data Analytics" will assist managers in providing an overview of the drivers for introducing big data technology into the organization and for understanding the types of business problems best suited to big data analytics solutions, understanding the value drivers and benefits, strategic planning, developing a pilot, and eventually planning to integrate back into production within the enterprise."

"One of the most exciting developments from the world of ideas in decades, presented with panache by two frighteningly brilliant, endearingly unpretentious, and endlessly creative young scientists."--Steven Pinker, author of The Better Angels of Our Nature Our society has gone from writing snippets of information by hand to generating a vast flood of 1s and 0s that record almost every aspect of our lives: who we know, what we do, where we go, what we buy, and who we love. This year, the world will generate 5 zettabytes of data. (That's a five with twenty-one zeros after it.) Big data is revolutionizing the sciences, transforming the humanities, and renegotiating the boundary between industry and the ivory tower. What is emerging is a new way of understanding our world, our past, and possibly, our future. In Uncharted, Erez Aiden and Jean-Baptiste Michel tell the story of how they tapped into this sea of information to create a new kind of telescope: a tool that, instead of uncovering the motions of distant stars, charts trends in human history across the centuries. By teaming up with Google, they were able to analyze the text of millions of books. The result was a new field of research and a scientific tool, the Google Ngram Viewer, so groundbreaking that its public release made the front page of The New York Times, The Wall Street Journal, and The Boston Globe, and so addictive that Mother Jones called it "the greatest timewaster in the history of the internet." Using this scope, Aiden and Michel-and millions of users worldwide-are beginning to see answers to a dizzying array of once intractable questions. How quickly does technology spread? Do we talk less about God today? When did people start "having sex" instead of "making love"? At what age do the most famous people become famous? How fast does grammar change? Which writers had their works most effectively censored by the Nazis? When did the spelling "donut" start replacing the venerable "doughnut"? Can we predict the future of human history? Who is better known-Bill Clinton or the rutabaga? All over the world, new scopes are popping up, using big data to quantify the human experience at the grandest scales possible. Yet dangers lurk in this ocean of 1s and 0s-threats to privacy and the specter of ubiquitous government surveillance. Aiden and Michel take readers on a voyage through these uncharted waters""Breaking open Big Data, two Harvard scientists reveal a ground-breaking way of looking at history and culture""

"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."

"E-commerce dan big data merupakan bukti dari kemajuan teknologi yang sangat pesat. Big data berperan cukup penting dalam perusahaan e-commerce untuk menangani perkembangan semua data, mengolah setiap data tersebut dan menjadi competitive advantage bagi perusahaan. Perusahaan XYZ.com mengalami kesulitan dalam menganalisis stok dan tren dari produk yang dijual. Jika hal ini tidak ditanggulangi, maka perusahaan XYZ.com akan kehilangan opportunity gain. Untuk menentukan tren dan stok produk secara cepat dengan akurat, dibutuhkan big data predictive analysis. Penelitian ini mengolah data transaksi menjadi data yang dapat dianalisis untuk menentukan tren dan prediksi tren produk berdasarkan kategorinya dengan menggunakan big data predictive analysis. Hasil dari penelitian ini akan memberikan informasi kepada pihak manajemen kategori apa yang berpotensi menjadi tren dan jumlah minimal stok yang harus disediakan dari kategori produk tersebut.

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E commerce and big data are evidence of rapid technological advances. Big data plays an important role in e commerce companies to handle and analyze all data changes, and become a competitive advantage for the company. XYZ.com experience a difficulty in analyzing stocks and commerce product trend. If this issue not addressed, XYZ.com company will lose an opportunity gain. To determine trends and stock accurately, XYZ.com can use big data predictive analysis. This study processes transaction data into data that can be analyzed to determine trends and predictions of product trends based on its categories using big data predictive analysis. The results of this study give massive informations to management about what categories will potential become trends and minimum stock required to be provided."