Sistem Informasi Pustaka

Manajemen Humas Pendidikan Islam

Teori dan Aplikasi

GEMAR BELAJAR AGAMA KRISTEN – Jilid 2

Buku Penunjang Pembelajaran Pendidikan Agama Kristen

Data Mining and Data Warehousing

Principles and Practical Techniques

Provides a comprehensive textbook covering theory and practical examples for a course on data mining and data warehousing.

Data Mining for Business Analytics

Concepts, Techniques and Applications in Python

Introduction to Data Mining and Analytics

Data Mining and Analytics provides a broad and interactive overview of a rapidly growing field. The exponentially increasing rate at which data is generated creates a corresponding need for profession
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Data Mining

The Textbook

Data Mining Techniques

For Marketing, Sales, and Customer Relationship Management

Many companies have invested in building large databases and data warehouses capable of storing vast amounts of information. This book offers business, sales and marketing managers a practical guide t
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Discovering Knowledge in Data

An Introduction to Data Mining

Encyclopedia of Machine Learning and Data Mining

Data Mining for Business Analytics

Concepts, Techniques, and Applications with XLMiner

Data Mining: Introductory And Advanced Topics

Statistics, Data Mining, and Machine Learning in Astronomy

A Practical Python Guide for the Analysis of Survey Data

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Manajemen Humas Pendidikan Islam

Teori dan Aplikasi

Citra positif merupakan hal terpenting yang harusdimiliki oleh suatu lembaga, termasuk lembaga sekolah.Citra positif tentunya akan berkaitan dengan eksistensi suatulembaga. Hal ini karena adanya penilaian yang positif akanmemengaruhi kepercayaan masyarakat terhadap suatusekolah untuk menyekolahkan anaknya di sekolah tersebut. Keberadaan sekolah pada awalnya mendasarkan pada suatualasan bahwa orangtua tidak mampu mendidik anaknyasecara sempurna dan lengkap, dengan demikianmemerlukan pihak lain, yakni lembaga sekolah, untuk membantu peran orangtua dalam mendidik anaknya, hal tersebut senada dengan apa yang disampaikan oleh Pidarta (2007: 176) bahwa keberadaan sekolah merupakan suatu lembaga yang hidup bersama-sama dengan warga masyarakat, di mana masyarakat membutuhkan agar para siswa dapat dibina di sekolah dan sebaliknya sekolah membutuhkan masyarakat untuk membantu kelancaran proses belajar mengajar.

Maskur,

Kurikulum SMP Islam Al-Azhar 14 Semarang Kurikulum di SMP Islam Al-Azhar 14 Semarang tentunya menyesuaikan tujuan lembaga untuk melahirkan cendekiawan-cendekiawan muslim yang handal, ... Pendidikan Agama Islam 2 2 2 24 2.

GEMAR BELAJAR AGAMA KRISTEN – Jilid 2

Buku Penunjang Pembelajaran Pendidikan Agama Kristen

Dunia pendidikan tak pernah luput dari perhatian setiap insane manusia karena ujung tombak pembangunan sebuah bangsa terletak dalam kerangka pendidikan bangsa tersebut. Pembangunan pendidikan suatu bangsa yang mengalami perkembangan kurang begitu signifikan tidak dapat menyeimbangkan laju pertumbuhan global yang sangat pesat sehingga bangsa tersebut menjadi tertinggal. Menyadari sangat besar peranan pendidikan dalam pembangunan suatu bangsa, penulis berupaya mengambil bagian tersebut. Sebab penulis sadar sebagai nara didik memiliki andil besar dalam menumbuhkan kecerdasan anak banga. Setiap ide pemikiran yang penulis miliki telah dituangkan dalam bahan ajar ‘Gemar Belajar Agama Kristen.’ Bahan ajar ini berupaya menuntun serta membimbing peserta didik bukan hanya kaya pengetahuan keagamaan secara kognitif semata melainkan kekayaan tersebut dalam segi afektif maupun psikomotorik.

Rifai, M.Th., M.Pd., M.Th., M.Pd.,

Dunia pendidikan tak pernah luput dari perhatian setiap insane manusia karena ujung tombak pembangunan sebuah bangsa terletak dalam kerangka pendidikan bangsa tersebut.

Data Mining and Data Warehousing

Principles and Practical Techniques

Provides a comprehensive textbook covering theory and practical examples for a course on data mining and data warehousing.

Parteek Bhatia,

Provides a comprehensive textbook covering theory and practical examples for a course on data mining and data warehousing.

Data Mining for Business Analytics

Concepts, Techniques and Applications in Python

Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python presents an applied approach to data mining concepts and methods, using Python software for illustration Readers will learn how to implement a variety of popular data mining algorithms in Python (a free and open-source software) to tackle business problems and opportunities. This is the sixth version of this successful text, and the first using Python. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes: A new co-author, Peter Gedeck, who brings both experience teaching business analytics courses using Python, and expertise in the application of machine learning methods to the drug-discovery process A new section on ethical issues in data mining Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students More than a dozen case studies demonstrating applications for the data mining techniques described End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology. “This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business specific procedures such as social network analysis and text mining. If not the bible, it is at the least a definitive manual on the subject.” —Gareth M. James, University of Southern California and co-author (with Witten, Hastie and Tibshirani) of the best-selling book An Introduction to Statistical Learning, with Applications in R

Galit Shmueli, Peter C. Bruce, Peter Gedeck, Nitin R. Patel, Peter C. Bruce, Peter Gedeck, Nitin R. Patel,

This is the sixth version of this successful text, and the first using Python.

Introduction to Data Mining and Analytics

Kris Jamsa,

Data Mining and Analytics provides a broad and interactive overview of a rapidly growing field.

Data Mining

The Textbook

This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues. It goes beyond the traditional focus on data mining problems to introduce advanced data types such as text, time series, discrete sequences, spatial data, graph data, and social networks. Until now, no single book has addressed all these topics in a comprehensive and integrated way. The chapters of this book fall into one of three categories: Fundamental chapters: Data mining has four main problems, which correspond to clustering, classification, association pattern mining, and outlier analysis. These chapters comprehensively discuss a wide variety of methods for these problems. Domain chapters: These chapters discuss the specific methods used for different domains of data such as text data, time-series data, sequence data, graph data, and spatial data. Application chapters: These chapters study important applications such as stream mining, Web mining, ranking, recommendations, social networks, and privacy preservation. The domain chapters also have an applied flavor. Appropriate for both introductory and advanced data mining courses, Data Mining: The Textbook balances mathematical details and intuition. It contains the necessary mathematical details for professors and researchers, but it is presented in a simple and intuitive style to improve accessibility for students and industrial practitioners (including those with a limited mathematical background). Numerous illustrations, examples, and exercises are included, with an emphasis on semantically interpretable examples. Praise for Data Mining: The Textbook - “As I read through this book, I have already decided to use it in my classes. This is a book written by an outstanding researcher who has made fundamental contributions to data mining, in a way that is both accessible and up to date. The book is complete with theory and practical use cases. It’s a must-have for students and professors alike!" -- Qiang Yang, Chair of Computer Science and Engineering at Hong Kong University of Science and Technology "This is the most amazing and comprehensive text book on data mining. It covers not only the fundamental problems, such as clustering, classification, outliers and frequent patterns, and different data types, including text, time series, sequences, spatial data and graphs, but also various applications, such as recommenders, Web, social network and privacy. It is a great book for graduate students and researchers as well as practitioners." -- Philip S. Yu, UIC Distinguished Professor and Wexler Chair in Information Technology at University of Illinois at Chicago

Charu C. Aggarwal,

This is a book written by an outstanding researcher who has made fundamental contributions to data mining, in a way that is both accessible and up to date. The book is complete with theory and practical use cases.

Data Mining Techniques

For Marketing, Sales, and Customer Relationship Management

Michael J. A. Berry, Gordon S. Linoff, Gordon S. Linoff,

Discovering Knowledge in Data

An Introduction to Data Mining

The field of data mining lies at the confluence of predictive analytics, statistical analysis, and business intelligence. Due to the ever-increasing complexity and size of data sets and the wide range of applications in computer science, business, and health care, the process of discovering knowledge in data is more relevant than ever before. This book provides the tools needed to thrive in today’s big data world. The author demonstrates how to leverage a company’s existing databases to increase profits and market share, and carefully explains the most current data science methods and techniques. The reader will “learn data mining by doing data mining”. By adding chapters on data modelling preparation, imputation of missing data, and multivariate statistical analysis, Discovering Knowledge in Data, Second Edition remains the eminent reference on data mining. The second edition of a highly praised, successful reference on data mining, with thorough coverage of big data applications, predictive analytics, and statistical analysis. Includes new chapters on Multivariate Statistics, Preparing to Model the Data, and Imputation of Missing Data, and an Appendix on Data Summarization and Visualization Offers extensive coverage of the R statistical programming language Contains 280 end-of-chapter exercises Includes a companion website for university instructors who adopt the book

Daniel T. Larose, Chantal D. Larose, Chantal D. Larose,

This book provides the tools needed to thrive in today’s big data world.

Encyclopedia of Machine Learning and Data Mining

This authoritative, expanded and updated second edition of Encyclopedia of Machine Learning and Data Mining provides easy access to core information for those seeking entry into any aspect within the broad field of Machine Learning and Data Mining. A paramount work, its 800 entries - about 150 of them newly updated or added - are filled with valuable literature references, providing the reader with a portal to more detailed information on any given topic.Topics for the Encyclopedia of Machine Learning and Data Mining include Learning and Logic, Data Mining, Applications, Text Mining, Statistical Learning, Reinforcement Learning, Pattern Mining, Graph Mining, Relational Mining, Evolutionary Computation, Information Theory, Behavior Cloning, and many others. Topics were selected by a distinguished international advisory board. Each peer-reviewed, highly-structured entry includes a definition, key words, an illustration, applications, a bibliography, and links to related literature.The entries are expository and tutorial, making this reference a practical resource for students, academics, or professionals who employ machine learning and data mining methods in their projects. Machine learning and data mining techniques have countless applications, including data science applications, and this reference is essential for anyone seeking quick access to vital information on the topic.

Claude Sammut, Geoffrey I. Webb, Geoffrey I. Webb,

Data Mining for Business Analytics

Concepts, Techniques, and Applications with XLMiner

An applied approach to data mining and predictive analytics with clear exposition, hands-on exercises, and real-life case studies. Readers will work with all of the standard data mining methods using the Microsoft® Office Excel® add-in XLMiner® to develop predictive models and learn how to obtain business value from Big Data. Featuring updated topical coverage on text mining, social network analysis, collaborative filtering, ensemble methods, uplift modeling and more, the Third Edition also includes: Real-world examples to build a theoretical and practical understanding of key data mining methods End-of-chapter exercises that help readers better understand the presented material Data-rich case studies to illustrate various applications of data mining techniques Completely new chapters on social network analysis and text mining A companion site with additional data sets, instructors material that include solutions to exercises and case studies, and Microsoft PowerPoint® slides https://www.dataminingbook.com Free 140-day license to use XLMiner for Education software Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition is an ideal textbook for upper-undergraduate and graduate-level courses as well as professional programs on data mining, predictive modeling, and Big Data analytics. The new edition is also a unique reference for analysts, researchers, and practitioners working with predictive analytics in the fields of business, finance, marketing, computer science, and information technology. Praise for the Second Edition "…full of vivid and thought-provoking anecdotes... needs to be read by anyone with a serious interest in research and marketing."– Research Magazine "Shmueli et al. have done a wonderful job in presenting the field of data mining - a welcome addition to the literature." – ComputingReviews.com "Excellent choice for business analysts...The book is a perfect fit for its intended audience." – Keith McCormick, Consultant and Author of SPSS Statistics For Dummies, Third Edition and SPSS Statistics for Data Analysis and Visualization Galit Shmueli, PhD, is Distinguished Professor at National Tsing Hua University’s Institute of Service Science. She has designed and instructed data mining courses since 2004 at University of Maryland, Statistics.com, The Indian School of Business, and National Tsing Hua University, Taiwan. Professor Shmueli is known for her research and teaching in business analytics, with a focus on statistical and data mining methods in information systems and healthcare. She has authored over 70 journal articles, books, textbooks and book chapters. Peter C. Bruce is President and Founder of the Institute for Statistics Education at www.statistics.com. He has written multiple journal articles and is the developer of Resampling Stats software. He is the author of Introductory Statistics and Analytics: A Resampling Perspective, also published by Wiley. Nitin R. Patel, PhD, is Chairman and cofounder of Cytel, Inc., based in Cambridge, Massachusetts. A Fellow of the American Statistical Association, Dr. Patel has also served as a Visiting Professor at the Massachusetts Institute of Technology and at Harvard University. He is a Fellow of the Computer Society of India and was a professor at the Indian Institute of Management, Ahmedabad for 15 years.

Galit Shmueli, Peter C. Bruce, Nitin R. Patel, Peter C. Bruce, Nitin R. Patel,

The book is a perfect fit for its intended audience." – Keith McCormick, Consultant and Author of SPSS Statistics For Dummies, Third Edition and SPSS Statistics for Data Analysis and Visualization "…extremely well organized, clearly ...

Data Mining: Introductory And Advanced Topics

Margaret H Dunham,

Statistics, Data Mining, and Machine Learning in Astronomy

A Practical Python Guide for the Analysis of Survey Data

As telescopes, detectors, and computers grow ever more powerful, the volume of data at the disposal of astronomers and astrophysicists will enter the petabyte domain, providing accurate measurements for billions of celestial objects. This book provides a comprehensive and accessible introduction to the cutting-edge statistical methods needed to efficiently analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the upcoming Large Synoptic Survey Telescope. It serves as a practical handbook for graduate students and advanced undergraduates in physics and astronomy, and as an indispensable reference for researchers. Statistics, Data Mining, and Machine Learning in Astronomy presents a wealth of practical analysis problems, evaluates techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. For all applications described in the book, Python code and example data sets are provided. The supporting data sets have been carefully selected from contemporary astronomical surveys (for example, the Sloan Digital Sky Survey) and are easy to download and use. The accompanying Python code is publicly available, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce all the figures and examples, evaluate the methods, and adapt them to their own fields of interest. Describes the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data sets Features real-world data sets from contemporary astronomical surveys Uses a freely available Python codebase throughout Ideal for students and working astronomers

Željko Ivezić, Andrew J. Connolly, Jacob T. VanderPlas, Alexander Gray, Andrew J. Connolly, Jacob T. VanderPlas, Alexander Gray,

This book provides a comprehensive and accessible introduction to the cutting-edge statistical methods needed to efficiently analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response ...

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