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"Whether you are brand new to data mining or working on your tenth predictive analytics project, Commercial data mining will be there for you as an accessible reference outlining the entire process and related themes. In this book, you'll learn that your organization does not need a huge volume of data or a Fortune 500 budget to generate business using existing information assets. Expert author David Nettleton guides you through the process from beginning to end and covers everything from business objectives to data sources, and selection to analysis and predictive modeling.Commercial data mining includes case studies and practical examples from Nettleton's more than 20 years of commercial experience. Real-world cases covering customer loyalty, cross-selling, and audience prediction in industries including insurance, banking, and media illustrate the concepts and techniques explained throughout the book."

"Whether you are brand new to data mining or working on your tenth predictive analytics project, Commercial Data Mining will be there for you as an accessible reference outlining the entire process and related themes. In this book, you'll learn that your organization does not need a huge volume of data or a Fortune 500 budget to generate business using existing information assets. Expert author David Nettleton guides you through the process from beginning to end and covers everything from business objectives to data sources, and selection to analysis and predictive modeling."

"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."

"This textbook presents a practical approach to predictive analytics for classroom learning. It focuses on using analytics to solve business problems and compares several different modeling techniques, all explained from examples using the SAS Enterprise Miner software. The authors demystify complex algorithms to show how they can be utilized and explained within the context of enhancing business opportunities. Each chapter includes an opening vignette that provides real-life example of how business analytics have been used in various aspects of organizations to solve issue or improve their results. A running case provides an example of a how to build and analyze a complex analytics model and utilize it to predict future outcomes."

"Learn 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."

"This book is a comprehensive introduction to the methods and algorithms and approaches of modern data analytics. It covers data preprocessing, visualization, correlation, regression, forecasting, classification, and clustering. It provides a sound mathematical basis, discusses advantages and drawbacks of different approaches, and enables the reader to design and implement data analytics solutions for real-world applications. Much of the content is based on the results of industrial research and development projects at Siemens."

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Sistem e-procurement merupakan sistem pengadaan yang dilakukan dengan cara elektronik yang menjadi faktor kunci untuk mengelola keuangan negara dengan kontrol yang tepat, serta dilindungi oleh kebijakan dan peraturan perundang-undangan yang berlaku. Menurut Asian Development Bank e-tendering, yang merupakan bagian dari e-procurement, merupakan aplikasi strategis yang dapat menunjang kinerja pada sektor pemerintahan. Berdasarkan temuan praktik yang tidak sesuai dengan prinsip pengadaan, maka dalam penelitian ini dilakukan sebuah analisis mendalam untuk mengevaluasi kegiatan tender pada lembaga kementerian di Indonesia. Pada penelitian ini ditunjukkan bagaimana penambangan data dilakukan pada portal pengadaan nasional untuk menganalisis data tender dan mendapatkan temuan pola tersembunyi yang berguna untuk pengambilan keputusan. Penelitian ini menggunakan metodologi web data mining dengan melakukan pendekatan analisis secara deskriptif dan statistik. Dengan melakukan metode uji chi-square dan multivariance Anova, ditemukan adanya kaitan antara lembaga kementerian dengan pemenang berulang pada tahun anggaran 2018-2019. Di samping itu frekuensi partisipasi peserta tidak memiliki dampak terhadap statistik kemenangan berulang pada Kementerian Perhubungan, tetapi berdampak pada Kementerian PUPR. Penelitian ini juga menemukan adanya hubungan yang sangat kuat antara variabel Harga Perkiraan Sendiri (HPS) dengan nilai pagu. Selain itu pada penelitian ini juga ditemukan anomali data pada harga penawaran pemenang tender dengan nilai 100 kali lebih besar dari harga pagu dan HPS.

 

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E-procurement is an electronic procurement system that became a key factor required to manage financial aspect of a country with appropriate controls, and protected by legal policies. According to Asian Development Bank, e-tendering as part of e-procurement, is classified as a strategic application that can enhance performance in the government sector. Based on the finding of practices that are not comply with the principles of good procurement governance, in this study an in-depth analysis was conducted to evaluate the tender activities of the ministry in Indonesia. This research shows how data mining is carried out at the national procurement portal to analyze tender data and findings the hidden pattern that would be useful for decision making. This study uses a web data mining methodology by conducting a descriptive and statistical analysis approach. By using the chi-square and multivariance Anova test method, this study has found that there was a relation between the ministries and repeated winners in year 2018-2019. In addition, the frequency of participation did not have an impact on the statistics of recurring wins at the Ministry of Transportation, but it had an impact on the Ministry of Public Works and Public Housing. This study also found a very strong relationship between the Owner Estimate (OE) value and the threshold value. In addition on this study, it was found anomaly data on the tender bid price of the winner with a value 100 times greater than the threshold value and OE value.

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