Data Mining Dan Data Warehouse Menggunakan Aplikasi Knime
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Data Mining Dan Data Warehouse Menggunakan Aplikasi Knime
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Author : Imam Tahyudin
language : id
Publisher: Zahira Media Publisher
Release Date : 2021-08-31
Data Mining Dan Data Warehouse Menggunakan Aplikasi Knime written by Imam Tahyudin and has been published by Zahira Media Publisher this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-31 with Computers categories.
Buku ini dibuka dengan pembahasan tentang konsep Data Mining, konsep Data Warehouse, dan pengenalan penggunaan Aplikasi KNIME. Selanjutnya dibahas berbagai metode yang digunakan dalam ML seperti Decision Tree, Random Forest, SVM, Naïve Bayes, KNN, Regresi Linier, Regresi Logistic, K-Means, dan FP-Growth. Buku ini menarik untuk dipelajari karena dijelaskan secara sederhana dan detail. Selain itu buku ini dilengkapi dengan implementasi menggunakan Aplikasi KNIME. Impelemntasi ini mudah untuk diikuti karena dijelaskan secara bertahap menggunakan software open-source yang powerfull dan mudah dipahami.
Buku Ajar Sistem Pendukung Keputusan
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Author : Gede Surya Mahendra
language : id
Publisher: PT. Sonpedia Publishing Indonesia
Release Date : 2023-09-07
Buku Ajar Sistem Pendukung Keputusan written by Gede Surya Mahendra and has been published by PT. Sonpedia Publishing Indonesia this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-07 with Computers categories.
Buku Ajar Sistem Pendukung Keputusan ini sebagai buku panduan komprehensif yang mengulas komponen-komponen penting pada mata kuliah Sistem Pendukung Keputusan. Buku ini dapat digunakan oleh pendidik dalam melaksanakan kegiatan pembelajaran khususnya keilmuan Komputer atau bidang Ilmu terkait lainnya. Buku ini umum dapat digunakan sebagai panduan dan referensi mengajar mata kuliah Sistem Pendukung Keputusan. Secara garis besar, buku ajar ini pembahasannya mulai dari Teori dan konsep pengambilan keputusan, Teori dan konsep DSS (gagasan aplikasi dss), Data warehousing, Multidimensional data model, Decision Analysis Introduction, Decision Analysis Model, Tipe pengambil keputusan, Tipe Forcasting, Performance Forecasting Method, dan di tutup dengan materi mengenai Penerapan AHP. Buku Ajar ini disusun secara sistematis, ditulis dengan bahasa yang jelas dan mudah dipahami, dapat digunakan dalam kegiatan pembelajaran mata kuliah Sistem Pendukung Keputusan.
Sensor Alami Tanaman Untuk Deteksi Suhu
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Author : Imam Tahyudin
language : id
Publisher: Zahira Media Publisher
Release Date : 2021-11-16
Sensor Alami Tanaman Untuk Deteksi Suhu written by Imam Tahyudin and has been published by Zahira Media Publisher this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-16 with Computers categories.
Tanaman bioelektrik potensial adalah tanaman yang menghasilkan sinyal listrik rendah karena aktivitas tanaman seperti fotosintesis dan pernafasan. Sinyal listrik tersebut akan berubah karena pengaruh faktor lingkungan seperti suhu, kelembaban dan perilaku manusia. Beberapa penulis sebelumnya berhasil menggunakan tanaman bioelektrik potensial untuk mendeteksi berbagai aktivitas manusia, seperti berjalan, bergerak, dan sebagainya. Selain itu, salah satu kelebihan tanaman bioelektrik potensial adalah mampu mengestimasi posisi seseorang di suatu ruangan. Untuk memperkirakan posisi ini, di antara penulis telah menggunakan beberapa metode seperti DT (Decision Tree), multi-layer perceptron, convolution neural network, model autoregresif, dan model analisis asosiasi. Akan tetapi, salah satu masalahnya adalah sulit untuk menemukan nilai akurasi terbaik untuk memperkirakan posisi dan adanya perlakuan berbeda jika dataset linier atau non linier. Sehingga beberapa peneliti melakukan eksperimen dan analysis lanjutan dengan berbagai pendekatan. Selain itu juga pembahasan tentang kecerdasan buatan dengan menggunakan tanaman bioelektrik potensial sangat menarik untuk digali lebih dalam. Diantaranya yaitu kemampuanya dalam mendeteksi perbedaan suhu. Oleh karena itu, pada buku referensi ini akan di bahas tentang beberapa metode dan pendekatan untuk analisis lanjutan hasil eksperimen tanaman bioelektrik potensial diantaranya yaitu pendekatan metode time series, machine learning, dan deep learning. Untuk proses eksperimen kami mengembangkan data logger dengan menggunakan raspberry yang digunakan untuk merekam respon tanaman bioelektrik potensial. Pada buku ini dijelaskan kemampuan tanaman bioelektrik potensial dalam mendeteksi perbedaan suhu di suatu ruangan. Metode yang digunakan adalah hibrid model SARIMA-LSTM dan metode kombinasi SARIMA-PARCD. Metode yang diusulkan ini mampu mengantisipasi dataset baik linier atau non linier. 183 Berdasarkan beberapa hasil penelitian tersebut diperoleh hasil bahwa eksperimen kecerdasan buatan pada tanaman bioelektrik potensial terbukti secara konsisten mampu mendeteksi perbedaan suhu dalam tiga kondisi yaitu kondisi panas, dingin, dan normal. Selanjutnya, kombinasi metode yang diusulkan menghasilkan nilai akurasi yang memuaskan. Untuk studi berikutnya pada buku ini kami awali dengan analisis deskripsi pasien COVID-19 di Banyumas dengan pendekatan machine learning. Pada road map penelitian ditahun berikutnya, kami akan melakukan implementasi tanaman bioelektrik potensial untuk deteksi suhu di kamar pasien Rumah sakit. Kami berharap selain manfaat fungsi kecerdasan buatan yang dihasilkan juga kemampuan alami tanaman sebagai fungsi healing yang dapat membantu proses penyembuhan pasien.
Data Mining For The Masses
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Author : Matthew North
language : en
Publisher:
Release Date : 2012-08-18
Data Mining For The Masses written by Matthew North and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-08-18 with Data mining categories.
Have you ever found yourself working with a spreadsheet full of data and wishing you could make more sense of the numbers? Have you reviewed sales or operations reports, wondering if there's a better way to anticipate your customers' needs? Perhaps you've even thought to yourself: There's got to be more to these figures than what I'm seeing! Data Mining can help, and you don't need a Ph.D. in Computer Science to do it. You can forecast staffing levels, predict demand for inventory, even sift through millions of lines of customer emails looking for common themes-all using data mining. It's easier than you might think. In Data Mining for the Masses, professor Matt North-a former risk analyst and database developer for eBay.com-uses simple examples, clear explanations and free, powerful, easy-to-use software to teach you the basics of data mining; techniques that can help you answer some of your toughest business questions. You've got data and you know it's got value, if only you can figure out how to unlock it. This book can show you how. Let's start digging! Through an agreement with the Global Text Project, an electronic version of this text is available online at (http://globaltext.terry.uga.edu/books). Proceeds from the sales of printed copies through Amazon enable the author to support the Global Text Project's goal of making electronic texts available to students in developing economies.
Python Data Science Handbook
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Author : Jake VanderPlas
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2016-11-21
Python Data Science Handbook written by Jake VanderPlas and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-21 with Computers categories.
For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms
Computer Networking
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Author : Olivier Bonaventure
language : en
Publisher: Lulu.com
Release Date : 2016-06-10
Computer Networking written by Olivier Bonaventure and has been published by Lulu.com this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-06-10 with Computers categories.
Original textbook (c) October 31, 2011 by Olivier Bonaventure, is licensed under a Creative Commons Attribution (CC BY) license made possible by funding from The Saylor Foundation's Open Textbook Challenge in order to be incorporated into Saylor's collection of open courses available at: http: //www.saylor.org. Free PDF 282 pages at https: //www.textbookequity.org/bonaventure-computer-networking-principles-protocols-and-practice/ This open textbook aims to fill the gap between the open-source implementations and the open-source network specifications by providing a detailed but pedagogical description of the key principles that guide the operation of the Internet. 1 Preface 2 Introduction 3 The application Layer 4 The transport layer 5 The network layer 6 The datalink layer and the Local Area Networks 7 Glossary 8 Bibliography
Predictive Analytics And Data Mining
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Author : Vijay Kotu
language : en
Publisher: Morgan Kaufmann
Release Date : 2014-11-27
Predictive Analytics And Data Mining written by Vijay Kotu and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-11-27 with Computers categories.
Put Predictive Analytics into ActionLearn the basics of Predictive Analysis and Data Mining through an easy to understand conceptual framework and immediately practice the concepts learned using the open source RapidMiner tool. Whether you are brand new to Data Mining or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Data Mining has become an essential tool for any enterprise that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, business intelligence and data warehousing professionals and for anyone who wants to learn Data Mining.You’ll be able to:1. Gain the necessary knowledge of different data mining techniques, so that you can select the right technique for a given data problem and create a general purpose analytics process.2. Get up and running fast with more than two dozen commonly used powerful algorithms for predictive analytics using practical use cases.3. Implement a simple step-by-step process for predicting an outcome or discovering hidden relationships from the data using RapidMiner, an open source GUI based data mining tool Predictive analytics and Data Mining techniques covered: Exploratory Data Analysis, Visualization, Decision trees, Rule induction, k-Nearest Neighbors, Naïve Bayesian, Artificial Neural Networks, Support Vector machines, Ensemble models, Bagging, Boosting, Random Forests, Linear regression, Logistic regression, Association analysis using Apriori and FP Growth, K-Means clustering, Density based clustering, Self Organizing Maps, Text Mining, Time series forecasting, Anomaly detection and Feature selection. Implementation files can be downloaded from the book companion site at www.LearnPredictiveAnalytics.com Demystifies data mining concepts with easy to understand language Shows how to get up and running fast with 20 commonly used powerful techniques for predictive analysis Explains the process of using open source RapidMiner tools Discusses a simple 5 step process for implementing algorithms that can be used for performing predictive analytics Includes practical use cases and examples
Data Mining Concepts And Techniques
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Author : Jiawei Han
language : en
Publisher: Elsevier
Release Date : 2011-06-09
Data Mining Concepts And Techniques written by Jiawei Han and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-06-09 with Computers categories.
Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining. This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining. - Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects - Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields - Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data
Discovering Knowledge In Data
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Author : Daniel T. Larose
language : en
Publisher: John Wiley & Sons
Release Date : 2005-01-28
Discovering Knowledge In Data written by Daniel T. Larose and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-01-28 with Computers categories.
Learn Data Mining by doing data mining Data mining can be revolutionary-but only when it's done right. The powerful black box data mining software now available can produce disastrously misleading results unless applied by a skilled and knowledgeable analyst. Discovering Knowledge in Data: An Introduction to Data Mining provides both the practical experience and the theoretical insight needed to reveal valuable information hidden in large data sets. Employing a "white box" methodology and with real-world case studies, this step-by-step guide walks readers through the various algorithms and statistical structures that underlie the software and presents examples of their operation on actual large data sets. Principal topics include: * Data preprocessing and classification * Exploratory analysis * Decision trees * Neural and Kohonen networks * Hierarchical and k-means clustering * Association rules * Model evaluation techniques Complete with scores of screenshots and diagrams to encourage graphical learning, Discovering Knowledge in Data: An Introduction to Data Mining gives students in Business, Computer Science, and Statistics as well as professionals in the field the power to turn any data warehouse into actionable knowledge. An Instructor's Manual presenting detailed solutions to all the problems in the book is available online.
Data Mining
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Author : Charu C. Aggarwal
language : en
Publisher: Springer
Release Date : 2015-04-13
Data Mining written by Charu C. Aggarwal and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-04-13 with Computers categories.
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