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"The definitive guide to successfully integrating social, mobile, Big-Data analytics, cloud and IoT principles and technologies The main goal of this book is to spur the development of effective big-data computing operations on smart clouds that are fully supported by IoT sensing, machine learning and analytics systems"

"ABSTRAKSebagai proses alamiah dalam mencapai titik ekuilibrium, perkembangan ekonomi digital akan selalu diikuti oleh peningkatan risiko keamanan cyber. Penelitian ini membahas analisis big data percakapan media sosial Twitter dengan tipe data yang tidak terstruktur untuk memprediksi risiko cyber berupa keberhasilan serangan exploit terhadap kerentanan sistem informasi yang dipublikasikan pada situs kerentanan global cvedetails.com common vulnerabilities and exposures CVE . Penelitian ini mengeksplorasi aspek kualitatif dan kuantitatif atas data yang bersumber dari twitter dan CVE menggunakan metode pemodelan algoritmik statistical machine learning. Prediksi dilakukan dengan membandingkan beberapa model klasifikasi yang dipilih dari sepuluh algoritma yang paling banyak digunakan dalam data mining berdasarkan survey yang dilakukan oleh IEEE pada International Conference on Data Mining tahun 2006. Hasil prediksi terbaik dihasilkan melalui model algoritma Artificial Neural Networks dengan tingkat akurasi 96,73 . Model prediksi dapat dimanfaatkan oleh perusahaan asuransi dengan produk perlindungan risiko cyber untuk mengurangi potensi penyebaran klaim terjadinya risiko. Model juga dapat dimanfaatkan oleh perusahaan umum untuk melakukan mitigasi risiko cyber secara efektif dan efisien dengan menghindari situasi false-negatives error dalam pengelolaan risiko.

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ABSTRACTAs a natural process in achieving equilibrium state, digital economic progress will always be followed by the increase of cyber security risk exposure. This research is focusing on unstructured Twitter social media big data analytics to predict cyber risks event in terms of successful attack on exploit based software vulnerability published in global vulnerability information websites cvedetails.com common vulnerabilities and exposures CVE . This research explores qualitative and quantitative aspect of data extracted from Twitter and CVE using statistical machine learning algorithmic modeling method. Prediction result obtained by comparing and selecting classification model from several statistical machine learning algorithm based on top ten algorithms in data mining survey produced by IEEE in 2006 International Conference on Data Mining. The best prediction results provided through Artificial Neural Networks algorithm with 96,73 accuracy rate. This prediction model offers advantages for insurance company with cyber liability product by reducing claim spread probability over cyber risk loss event. Prediction model can also be beneficial for company in general by providing an effective and efficient way to mitigate cyber risks through false negatives error avoidance in risk management."

"This book provides users with cutting edge methods and technologies in the area of big data and visual analytics, as well as an insight to the big data and data analytics research conducted by world-renowned researchers in this field. The authors present comprehensive educational resources on big data and visual analytics covering state-of-the art techniques on data analytics, data and information visualization, and visual analytics. Each chapter covers specific topics related to big data and data analytics as virtual data machine, security of big data, big data applications, high performance computing cluster, and big data implementation techniques. Every chapter includes a description of an unique contribution to the area of big data and visual analytics. This book is a valuable resource for researchers and professionals working in the area of big data, data analytics, and information visualization. Advanced-level students studying computer science will also find this book helpful as a secondary textbook or reference."

"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."

"In line with rapid business process digitalization in the Directorate General of Taxes, the size of the data stored in the institution has grown exponentially. However, there is a problem with generating value out of the valuable data assets. Correspondingly, this research provides machine-learning-based predictive analytics as a solution to the question of how to use taxpayers' trigger data as a decision support system to discover and realize unexplored tax potential. More specifically, this research presents predictive analytics models that can accurately predict which potential taxpayers are likely to pay their due. We developed three machine learning models: logistic regression, random forest, and decision tree. We analyzed 5,562 tax revenue potential data samples with eight predictors: trigger data nominal value, distance to tax office, type of taxpayer, media of tax report, type of tax, report status, registered year of taxpayer, and area coverage. Our study shows that the random forest model provided the best prediction performance. The resultant weight of each attribute indicated that the status of the tax report was the top tier of variable importance in predicting tax revenue potential. The analytics can help tax officers determine potential taxpayers with the highest likelihood to pay their due. Given the size of the data records, this approach can provide tax administrators with a powerful tool to increase work efficiency, combat tax evasion, and provide better customer service."

"The book describes the emergence of big data technologies and the role of Spark in the entire big data stack. It compares Spark and Hadoop and identifies the shortcomings of Hadoop that have been overcome by Spark. The book mainly focuses on the in-depth architecture of Spark and our understanding of Spark RDDs and how RDD complements big data’s immutable nature, and solves it with lazy evaluation, cacheable and type inference. It also addresses advanced topics in Spark, starting with the basics of Scala and the core Spark framework, and exploring Spark data frames, machine learning using Mllib, graph analytics using Graph X and real-time processing with Apache Kafka, AWS Kenisis, and Azure Event Hub. It then goes on to investigate Spark using PySpark and R. Focusing on the current big data stack, the book examines the interaction with current big data tools, with Spark being the core processing layer for all types of data.The book is intended for data engineers and scientists working on massive datasets and big data technologies in the cloud. In addition to industry professionals, it is helpful for aspiring data processing professionals and students working in big data processing and cloud computing environments."

"Access to big data, the “new commodity” for the 21st century economies, and its uses and potential abuses, has both conceptual and methodological impacts for the field of comparative and international education. This book examines, from a comparative perspective, the impact of the movement from the so-called knowledge-based economy towards the Intelligent Economy, which is premised upon the application of knowledge. Knowledge, the central component of the knowledge-based economy, is becoming less important in an era that is projected to be dominated and defined by the integration of complex technologies under the banner of the fourth industrial revolution. In this new era that blends the physical with the cyber-physical, the rise of education intelligence means that clients including countries, organizations, and other stakeholders are equipped with cutting-edge data in the form of predicative analytics, and knowledge about global educational predictions of future outcomes and trends. In this sense, this timely volume links the advent of this new technological revolution to the world of governance and policy formulation in education in order to open a broader discussion about the systemic and human implications for education of the emerging intelligent economy. By providing a unique comparative perspective on the Educational Intelligent economy, this book will prove invaluable for researchers and scholars in the areas of comparative education, artificial intelligence and educational policy."

"Data are the prominent elements in scientific researches and approaches. Data Science methodology is used to select and to prepare enormous numbers of data for further processing and analysing. Big Data technology collects vast amount of data from many sources in order to exploit the information and to visualise trend or to discover a certain phenomenon in the past, present, or in the future at high speed processing capability. Predictive analytics provides in-depth analytical insights and the emerging of machine learning brings the data analytics to a higher level by processing raw data with artificial intelligence technology. Predictive analytics and machine learning produce visual reports for decision makers and stake-holders. Regarding cyberspace security, big data promises the opportunities in order to prevent and to detect any advanced cyber-attacks by using internal and external security data."

"E-commerce merupakan online platform yang sedang mengalami pertumbuhan pesat dan memberikan kontribusi terhadap perekonomian internet di Indonesia selama lima tahun terakhir. E-commerce menghasilkan ulasan konsumen yang merupakan sumber informasi bagi para pemangku kepentingan. Penelitian ini melakukan analisis big data terhadap 132.085 ulasan konsumen online mengenai ponsel Xiaomi yang ditulis pada tiga situs e-commerce terbesar di Indonesia: Shopee, Bukalapak, dan Blibli dengan text mining, untuk mengidentifikasi distribusi topik, menganalisis jaringan asosiasi semantik, menemukan perbedaan pada ketiga situs, dan menganalisis hubungan antara topik dan skor penilaian ulasan. Hasil penelitian menunjukkan bahwa logistik merupakan topik yang paling banyak didiskusikan pada ketiga situs, sementara kualitas pelayanan lebih banyak didiskusikan pada Consumer-to-Consumer (C2C) daripada Business-to-Consumer (B2C) e-commerce. Atribut ponsel lebih banyak didiskusikan pada Bukalapak dan Blibli, dengan fokus topik sistem dan CPU & perangkat keras. Jaringan ulasan konsumen Bukalapak membentuk scale-free network, sementara jaringan kedua situs lainnya hanya menunjukkan karakteristik dari small-world network. Hasil regresi logistik ordinal menunjukkan bahwa 5 dari 8 topik yang dibahas dalam komentar ulasan memiliki hubungan negatif dengan skor penilaian, serta ulasan bernilai rendah cenderung memiliki komentar yang lebih panjang dan spesifik. Hasil penelitian dapat bermanfaat sebagai wawasan untuk pengembangan bagi para pemangku kepentingan di industri e-commerce.

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E-commerce is a rapidly growing online platform that contributes to Indonesias internet economy during the past five years. E-commerce generates customer reviews as a source of information for stakeholders. This study applies big data analytics toward 132,085 online reviews about Xiaomi mobile phones posted on three major e-commerce websites in Indonesia: Shopee, Bukalapak, and Blibli by text mining, in identifying their distribution of topics, analyzing semantic association network, determining differences between the three websites, also analyzing the relationship between topics and rating score. The findings show that logistics is the most highly discussed topic, while service quality is discussed more in Consumer-to-Consumer (C2C) rather Business-to-Consumer (B2C) e-commerce. Phone attributes are discussed more in Bukalapak and Blibli, focusing on system and CPU & hardware topics. The network of Bukalapaks customer reviews form a scale-free network, and the other two only have the characteristics of a small-world network. The overall results from multilinear regression and ordinal logistic regression show that 5 out of 8 topics reviewed have negative relationships with rating scores, and low-rated reviews tend to have longer and more specific review comments. The findings provide insights for e-commerce stakeholders in supporting further development."

"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. "