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"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."

"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"

"Cloud computing pada instansi pemerintah dapat membawa dampak positif bagi layanan publik pemerintah dan ekonomi regional. Cloud computing diharapkan menjadi solusi sistem TI yang efisisen dan ramah lingkungan dalam rangka mempercepat implementasi e-Government. Penggunaan teknologi cloud computing pada Badan Siber dan Sandi Negara BSSN yang memiliki kantor di beberapa lokasi diharapkan dapat meningkatkan efisiensi kinerja. Beberapa unit kerja pada BSSN sudah menggunakan aplikasi cloud storage namun belum terintegrasi dan belum dipastikan apakah telah memenuhi standar keamanan BSSN. Di satu sisi, BSSN telah mengembangkan prototype aplikasi cloud storage yang aman sesuai standar keamanan BSSN. Oleh karena itu, perlu dirumuskan perencanaan strategi layanan cloud storage pada BSSN dengan menggunakan prototype aplikasi yang sudah ada agar terintegrasi dan terpenuhinya standar keamanan BSSN. Penelitian ini melakukan perencanaan strategi dengan menggunakan tahap formulasi strategi pada kerangka manajemen strategis. Formulasi strategi terdiri atas audit eksternal, audit internal, analisis matriks EFE, analisis matriks EFE, analisis matriks IE, analisis SWOT, dan analisis QSPM. Hasil dari analisis QSPM inilah yang menghasilkan rekomendasi prioritas strategi bagi BSSN dalam menerapkan cloud storage di lingkungan internalnya.Terdapat 5 lima alternatif strategi yang dihasilkan dari tahap-tahap pada formulasi strategi. Setelah dilakukan penilaian pada matriks QSPM dipilih 3 tiga alternatif strategi yang memiliki nilai total attaractiveness score TAS tertinggi. Ketiga alternatif strategi tersebut adalah melengkapi sumber daya TIK dan SDM secara bertahap, membuat grand design TIK, dan menyempurnakan prototype cloud storage.

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Cloud computing at government institutions can have positive impacts on government public services and regional economies. Cloud computing is expected to be an efficient and eco friendly IT system solution in order to accelerate e Government implementation. The use of cloud computing technology in National Cyber and Crypto Agency NCCA , which has several offices, is expected to improve itself performance efficiency.

Some departments on NCCA are already using cloud storage applications but not yet integrated and have not been ascertained whether they meet NCCA security standards. On the one hand, NCCA has developed a secure prototype cloud storage application that complies with NCCA security standards. Therefore, it is necessary to formulate a cloud storage service strategy in NCCA using existing application prototype to integrate and fulfill BSSN security standard.

This research performs strategic planning using strategy formulation phase in strategic management framework. The strategy formulation consists of external audit, internal audit, EFE matrix analysis, EFE matrix analysis, IE matrix analysis, SWOT analysis, and QSPM analysis.

The results of QSPM analysis is strategic priority recommendations for NCCA in implementing cloud storage in its internal environment.There are 5 five alternative strategies generated from the stages in the strategy formulation. After the QSPM analysis, 3 three alternative strategies which have highest total attractiveness score TAS were selected. These three alternative strategies are equipping ICT and human resources gradually, creating a grand design of ICT, and improving cloud storage prototype. "

"Cloud computing merupakan teknologi mutakhir yang dapat digunakan oleh organisasi dalam mendukung proses bisnisnya. Cloud computing memungkinkan organisasi melakukan efisiensi biaya dan pengelolaan sistem TI. Lembaga Pengembangan Uji Kompetensi akan memanfaatkan Teknologi cloud untuk membangun Sistem Uji Kompetensi Tenaga Kesehatan Nasional. Tujuan dari penggunaan teknologi cloud adalah untuk memudahkan pengelolaan infrastruktur Sistem Uji Kompetensi Tenaga Kesehatan Nasional dengan tetap terpenuhinya kebutuhan keamanan sistem. Penelitian ini akan membahas perancangan arsitektur keamanan Sistem Uji Kompetensi Tenaga Kesehatan Nasional berbasis Infrastructure as a Service dengan menggunakan NIST Cloud-adapted Risk Management Framework dari dokumen NIST SP 500-299 mengenai Arsitektur Referensi Keamanan Cloud.

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Cloud computing is cutting-edge technology that can be used by organization to support its business processes. Cloud computing allows organization to do efficiency for IT system cost and management. Lembaga Pengembangan Uji Kompetensi will use Cloud technology to build a Sistem Uji Kompetensi Tenaga Kesehatan Nasional. The goal of cloud technology use is to simplify the infrastructure management of Sistem Uji Kompetensi Tenaga Kesehatan Nasional while fulfilling the system security requirement. This study will discuss cloud security architecture design for Sistem Uji Kompetensi Tenaga Kesehatan Nasional on Infrastructure as a Service using NIST Cloud-adapted Risk Management Framework from NIST SP 500-299 documents about Cloud Security Reference Architecture."

"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."

"Penelitian yang dilakukan adalah pengembangan program pendeteksi plagiarisme otomatis dwibahasa dengan waktu pelaksanaan yang singkat. Penggunaan MySQL sebagai sistem basis data menyebabkan program membutuhkan waktu yang lama untuk menyelesaikan eksekusinya, sehingga pada penelitian ini sistem basis data diganti pada program. Sistem database yang dipilih adalah Redis karena Redis menyimpan data dalam memori, sehingga diharapkan pengambilan data dapat dilakukan dengan lebih cepat. Pada akhir penelitian, berdasarkan perbandingan program yang menggunakan database Redis dengan program yang menggunakan database MySQL didapatkan bahwa penggunaan database Redis membuat eksekusi program lebih cepat sekitar 2999.335% hingga 3050.966% dibandingkan program yang digunakan. database MySQL. Pada penelitian ini juga dibuat sistem antarmuka pengguna grafis untuk program deteksi plagiarisme. Hal ini bertujuan agar pengguna lebih mudah dalam menggunakan program dan pengguna dapat langsung mengunggah makalah yang ingin diuji tingkat plagiarismenya. Pengujian pada sistem antarmuka program dilakukan dengan menanyakan penilaian terhadap 30 pengguna yang telah menggunakan program. Hasil pengujian pada antarmuka ini mendapatkan nilai akhir sebesar 87.93% di evaluasi alat ukur oleh pengguna sehingga termasuk dalam kategori Sangat Baik. Sedangkan pada pengukuran skala usability sistem, desain antarmuka program deteksi plagiarisme memperoleh skor akhir 79,16 dan termasuk dalam kategori Baik.

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The research conducted is the development of a bilingual automatic plagiarism detection program with a short implementation time. The use of MySQL as a database system causes the program to take a long time to complete its execution, so that in this study the database system was replaced in the program. The database system chosen is Redis because Redis stores data in memory, so it is hoped that data retrieval can be done more quickly. At the end of the study, based on a comparison of programs using the Redis database and the programs using the MySQL database, it was found that the use of the Redis database made program execution faster by around 2999.335% to 3050.966% compared to the programs used. MySQL database. In this research, a graphical user interface system for the plagiarism detection program was also developed. This aims to make it easier for users to use the program and users can directly upload the paper they want to test for plagiarism level. Testing on the program interface system is carried out by asking for an assessment of 30 users who have used the program. The test results on this interface get a final score of 87.93% in the evaluation of the measuring instrument by the user so that it is included in the Very Good category. Whereas in the measurement of the system usability scale, the plagiarism detection program interface design obtained a final score of 79.16 and was included in the Good category."

"The objective of this book is to provide the reader with a comprehensive survey of the topic compressed sensing in information retrieval and signal detection with privacy preserving functionality without compromising the performance of the embedding in terms of accuracy or computational efficiency. The reader is guided in exploring the topic by first establishing a shared knowledge about compressed sensing and how it is used nowadays. Then, clear models and definitions for its use as a cryptosystem and a privacy-preserving embedding are laid down, before tackling state-of-the-art results for both applications. The reader will conclude the book having learned that the current results in terms of security of compressed techniques allow it to be a very promising solution to many practical problems of interest. The book caters to a broad audience among researchers, scientists, or engineers with very diverse backgrounds, having interests in security, cryptography and privacy in information retrieval systems. Accompanying software is made available on the authors’ website to reproduce the experiments and techniques presented in the book. The only background required to the reader is a good knowledge of linear algebra, probability and information theory."

"The book constitutes selected high quality papers presented in International Conference on Computing, Power and Communication Technologies 2018 (GUCON 2018) organised by Galgotias University, India, in September 2018. It discusses issues in electrical, computer and electronics engineering and technologies. The selected papers are organised into three sections - cloud computing and computer networks; data mining and big data analysis; and bioinformatics and machine learning. In-depth discussions on various issues under these topics provides an interesting compilation for researchers, engineers, and students."

"Turbin gas adalah suatu alat yang memanfaatkan gas sebagai fluida untuk memutar turbin dengan pembakaran internal sehingga mampu memutar generator untuk menghasilkan listrik. Turbin gas memiliki tingkat bahaya yang besar, sehingga perlu dilakukan penelitian untuk menganalisis seberapa besar potensi kegagalan komponen-komponennya. Jika sebuah mesin atau peralatan mengalami kerusakan, maka seluruh fungsi akan terhenti. Oleh karena itu, aktivitas preventive maintenace dibutuhkan untuk mencegah kerusakan dan meminimasi downtime. Tahapan penelitian ini dimulai dengan menentukan komponen kritis menggunakan diagram pareto. Kemudian memvisualisasikan data-data yang didapat. Lalu, menentukan nilai parameter shape (β), parameter scale (η), reabilitas, MTTF (Mean Time to Failure), dari komponen-komponen kritis. Terakhir merekomendasikan jadwal preventive maintenance. Dalam penelitian ini pengolahan dan analisis data dilakukan melalui Big Data Analytics menggunakan R Software diharapkan kedepannya dapat dikembangkan menjadi sebuat aplikasi yang terintegrasi untuk mengimpor dan menganalisis data historis (data base), memudahkan untuk memprediksi kegagalan secara real time, memprediksi kegagalan sebelum muncul, dan dapat mengawasi equipment secara run on live. Berdasarkan hasil pengolahan data yang telah dilakukan, ditemukan bahwa ada 8 komponen kritis, Penentuan keandalan yang dilakukan dengan bantuan R software dengan menggunakan distribusi weibull menunjukkan saat 43.830 jam operasional atau 5 tahun, komponen yang memiliki keandalan paling rendah adalah Actuator dengan nilai sebesar 0,799. Keandalan sistem pada saat 43.830 jam atau 5 tahun adalah 0,866, nilai ini digolongkan sebagai kuat. Hasil dari evaluasi nilai parameter shape (β), menunjukan 7 dari 8 komponen di kategorikan IFR (Increasing Failure Rate) kegagalan ini diakibatkan oleh beberapa faktor seperti penuaan, korosi, gesekan, sehingga di sebut fase pengausan (wearout), dan solusi yang tepat untuk membuat rekomendasi jadwal preventive maintenance dengan T=80%.

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Gas turbine is one tool that uses gas as a fluid to turn turbines with internal combustion so that it is able to turn generators to produce electricity. Gas turbines have a high level of danger, so research needs to be done to increase the high potential level of its components. If the machine is damaged, all functions will stop. Therefore, preventive activities are needed to prevent damage and minimize downtime. The stages of this research began by determining the critical components using pareto diagrams. Then visualize the data obtained. Then, determine the value of the form parameter (β), parameter scale (η), reliability, MTTF (Mean Time to Failure), from the critical components. Last scheduled preventative maintenance schedule. In this research, processing and analyzing data done through Big Data Analytics using R Software is expected to be developed in the future into an integrated application to facilitate and analyze historical data (databases), facilitate to predict in real time, predict changes before they appear, and Can keep running equipment directly. Based on the results of data processing that has been done, found that there are 8 critical components, Determination which is done with the help of R software using Weibull distribution shows when 43,830 operational hours or 5 years, the component that adds the lowest is the Actuator with a value of 0.799. The current system value of 43,830 hours or 5 years is 0.866, this value is classified as strong. The results of the evaluation of the form parameter values (β), showed 7 out of 8 components categorized as IFR (Increased Failure Rate) this improvement was caused by several factors such as aging, corrosion, friction, so it was called the wearout phase, and the solution needed for make a preventive maintenance schedule recommendation with T = 80%."

"ABSTRAKNama : Adi MulyadiProgram studi : Magister ManajemenJudul : Analisis Segmentasi Konsumen Pada Perusahaan Real Estate Menggunakan Big Data Analytics Studi pada PT. ISPI Pratama LestariPembimbing : Arga Hananto, M.Bus. Studi tentang segmentasi konsumen dipengaruhi oleh kebutuhan perusahaan untuk bersaing dengan kompetitornya dan menciptakan keunggulan kompetitif bagi perusahaannya. Segmentasi produk merupakan salah satu hal utama dalam dunia bisnis, karena kesalahpahaman dalam segmentasi konsumen dapat mengakibatkan berkurangnya pendapatan. Real estate merupakan industri senilai milyaran dolar yang sangat tersegmentasi, dikarenakan karakteristik konsumennya yang beragam. Indonesia merupakan pasar yang potensial dan bertumbuh bagi industri real estate dan perumahan, karena Indonesia memiliki jumlah penduduk yang besar sekitar 260 juta jiwa dan memiliki area geografis yang luas. Untuk menganalisa data dengan jumlah besar tersebut, perusahaan real estate menggunakan Big Data Analytics, sebagai alat untuk mendapatkan masukan yang berarti dari data tersebut. Big Data mulai banyak digunakan sebagai alat untuk mempelajari tentang kondisi atau untuk memprediksi perilaku yang mungkin terjadi melalui berbagai pemodelan analisis data. Penelitian ini menyajikan analisis segmentasi untuk membantu perusahaan pengembang real estate dalam memahami segmentasi konsumen mereka, dengan menggunakan data transaksi penjualan perusahaan periode 2013 - 2017. Analisis segmentasi dalam penelitian ini telah dikembangkan menggunakan cluster analysis, dengan menggunakan metode hierarchical clustering, Elbow Method, dan K-Means. Hasil dari cluster analysis menunjukkan bahwa terdapat 4 segmen konsumen, yang memiliki karakteristik demografis dan preferensi produk yang berbeda. Selain itu, penelitian ini juga melakukan analisis tabulasi silang untuk mengetahui hubungan antar variabel. Selanjutnya dilakukan analisis diskriminan, dari situ diketahui bahwa gaji dan harga jual merupakan 2 variabel yang secara signifikan memberikan pengaruh paling besar terhadap penentuan cluster membership. Setelah mengetahui karakteristik dan melakukan analisa, dapat diusulkan bentuk promosi yang sesuai bagi masing ndash; masing segmen.Kata kunci:Segmentasi konsumen, real estate, big data, cluster analysis, tabulasi silang

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ABSTRACTABSTRACT Name Adi MulyadiStudy Program Magister of ManagementTitle Customer Segmentation Analysis In Real Estate Using Big Data Analytics A Study In PT. ISPI Pratama LestariCounsellor Arga Hananto, M.Bus. The study of consumer segmentation is influenced by a company 39 s need to compete with its competitors and create a competitive advantage. Product segmentation is one of the main things in the business world, because misunderstanding in consumer segmentation can lead to reduced revenue. Real estate is a multi billion dollar industry that is highly segmented, due to the diverse characteristics of its customers. Indonesia is a potential and growing market for the real estate and housing industries, as Indonesia has a large population around 260 million people and has a large geographical area. To analyze such big amounts of data, real estate companies use Big Data Analytics, as a means to gain meaningful insight from the data. Big Data is widely used as a tool to learn about conditions or to predict behaviors that may occur through various data analysis models. This study presents segmentation analysis to help real estate developers to understand their customer segmentation using company sales transaction data from 2013 to 2017 period. Segmentation analysis in this research has been developed using cluster analysis, with hierarchical clustering, Elbow Method, and K Means. The results of cluster analysis show that there are 4 segments of consumers, which have different demographic characteristics and product preferences. In addition, this study also conducted cross tabulation analysis to determine the relationship between variables. Then from discriminant analysis, it is known that salary and selling price are 2 variables that significantly give the most influence on cluster membership determination. After knowing the characteristics and perform the analysis, it can be proposed the appropriate form of promotion for each segment. Key words Customer segmentation, real estate, big data, cluster analysis, cross tabulation"