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"Big Data and Decision-Making: Applications and Uses in the Public and Private Sector breaks down the concept of big data to reveal how it has become integrated into the fabric of both public and private domains, as well as how its value can ultimately be exploited."

"Penelitian ini mengambil salah satu contoh penggunaan Big Data Analytics di perusahaan dan bagaimana pengimplementasian Big Data Analytics dapat membantu Manajer untuk mengambil keputusan dari data yang diambil. Dalam hal ini, Dataset yang diambil adalah data aktivitas pengguna situs web e-commerce yang memiliki fitur penjualan produk perusahaan melalui situs web. Dalam prosesnya, aktivitas pengguna situs web seperti page visit, penambahan produk ke keranjang belanja, dan pembelian produk produk akan dikumpulkan. Dari data yang dikumpulkan, laporan akan dibuat untuk diberikan kepada Manajer. Penelitian ini  akan mengidentifikasi poin yang dapat dicatat dari laporan yang dihasilkan. Seperti bagaimana performa penjualan dari produk tertentu, indentifikasi hubungan antar produk (apakah satu produk tertentu tergantung pada produk lain), Dan mengidentifikasi perilaku pengguna terhadap pembelian produk. Penelitian ini juga akan mengidentifikasi apakah implementasi Big Data yang ada di perusahaan saat ini dapat ditingkatkan, dan mengidentifikasi apakah peningkatan sistem implementasi Big Data merupakan investasi yang baik dan bermanfaat bagi perusahaan.

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This paper takes one example of Big Data Analytics usage on a company and how it can help Managers to take decision from the data taken. In this case, the Big Data taken would be the data of user activities of an e-commerce website which holds features to sell the company products through the website. In the process, the user activities of the website such as website visits, user clicking the add to cart button, and proceed on buying the product will be collected. From the data collected, a report will be created to be shown to the Managers. This paper will specifically identify the points to be noted from the generated report. Such as how is the sales for a specific product, identify the relations between products (either one product is dependent to other product), and identify specific behavior of user towards product purchases. This paper would also identify whether the current Big Data implementation on the company can be improved, and identify if it is a good investment for the company to improve the Big Data implementation system."

"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."

"Dalam Tesis ini penulis menilai harga wajar saham PT Aqua Golden Mississippi Tbk pada saat melakukan go private dengan menggunakan data proyeksi tahun 2005-2010 dan data keuangan Actual tahun 2005-2007, yang ternyata hanya dapat direalisasi berkisar antara 38% - 50 % saja. Penilaian harga wajar saham dilakukan dengan menggunakan 3 metode pendekatan yakni pendekatan income yang dalam hal ini menggunakan Dis- counted Cash Flow (DCF) dan Dividend Discount Model (DDM), dan sebagai pembanding, juga menggunakan pendekatan asset yakni Net Asset Value (NAV) dan pendekatan pasar dengan menggunakan Price Earnings Ratio (PER) untuk industri makanan dan minuman. Dengan menggunakan ketiga metode pendekat- an tersebut diharapkan lebih mendekati harga wajar saham PT Aqua Golden Mississippi Tbk yang pada saat proses go private menawarkan harga pembelian kembali (buyback) sebesar Rp 100.000 per lembar. Berdasarkan ketiga pendekatan tersebut diperoleh penilaian harga wajar saham: (1) dengan Discounted Cash Flow (DCF) sebesar Rp 139.350 per lembar. Namun karena proyeksi dinilai terlalu agresif, yang hanya dapat dicapai berkisar antara 38% - 50% untuk tahun 2005 – 2007, maka harga wajar saham di prorata menjadi Rp 69.675 per lembar. (2) dengan Dividend Discount Model (DDM) menghasilkan harga wajar sebesar Rp 18.859 per lembar. Penilaian dengan metode ini sangat tergantung pada kebijakan dalam membayar dividen, bukan berdasarkan profitabilitas. (3) dengan Net Asset Value (NAV), yang menghasil- kan harga wajar sebesar Rp 32.969 per lembar, dan (4) dengan Price Earnings Ratio (PER) untuk industri makanan dan minuman menghasilkan harga wajar sebesar Rp 70.739 per lembar. Dengan menggunakan keempat metode tersebut, metode Discounted Cash Flow dan Price Earnings Ratio dianggap paling mendekati harga wajar saham. Untuk itu dapat disimpulkan bahwa harga penawaran pembelian kembali (buyback) oleh Perseroan sebesar Rp 100.000 per lembar sudah diatas harga wajar.

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In this thesis I make a valuation of PT Aqua Golden Mississippi Tbk ‘s share fair value exactly as at the company took corporate action for going private, using the company’s projection data for 2005-2010 and the actual financial data for 2005-2007 which can only be realized about 38% to 50% during the period.

The valuation of the share fair value uses three approaches: income approach which uses Discounted Cash Flow (DCF) and Dividend Discount Model (DDM), while other approaches are used for a comparation purpose: assets approch, uses Net Asset Value (NAV) and market approach, uses Price Earnings Ratio (PER) of food and beverages industry. The three appoaches are expected can give an appropriate result of PT Aqua Golden Mississippi Tbk’s share market fair value, in which the company offered buyback share price for IDR 100.000,- as at the going private action was taken.

Based on the three approaches, result of the share fair value can be described as follow: (1) Discounted Cash Flow (DCF) for IDR 139.350 per share, but, since the projection is consider as too agresive, on which the realization figure during 2005-2007 were only 38% to 50% , therefor the fair value should be about IDR 69.675 per share. (2) Dividend Discount Model (DDM) for IDR 18.859 per share. This valuation method depands on the company’s policy to determine the dividend amount per share, and not depands on the profitabilities. (3) Net Asset Value (NAV), for IDR 32.969 per share, and (4) Price Earnings Ratio (PER) for foods and beverages industry, for IDR 70.739 per share.

Among the four method, the Discounted Cash Flow and the Price Earnings Ratio are treated as better methods then others and therefor the rusult can reflect the market fair value. Finally I can make a conclusion that the buyback price offered by the company for IDR Rp 100.000,- was above the fair value."

"Analisis triclustering adalah salah satu metode data mining yang memiliki tujuan mengelompokkan data berbentuk tiga dimensi. Triclustering umumnya digunakan pada bidang bioinformatika untuk menganalisis kesamaan ekspresi gen suatu eksperimen pada titik waktu tertentu. Analisis triclustering yang dilakukan pada penelitian ini menggunakan metode gabungan Fuzzy Cuckoo Search berdasarkan Gaussian Distribution dengan -Trimax. Metode ini merupakan penggabungan algoritma nodes deletion pada Trimax dengan algoritma optimasi Fuzzy Cuckoo Search. Algoritma nodes deletion pada -Trimax digunakan pada fase pembentukan populasi awal tricluster. Konsep algoritma nodes deletion yaitu dapat menghasilkan himpunan tricluster dengan Mean Square Residue (MSR) di bawah threshold dan mendekati 0. Algoritma optimasi Cuckoo Search adalah algoritma pencarian solusi tricluster, digambarkan dengan konsep parasitisme spesies burung cuckoo. Pada penelitian ini, Cuckoo Search menggunakan random walk Gaussian Distribution untuk pencarian solusi tricluster. Berdasarkan hal ini komputasi algoritma Cuckoo Search menjadi lebih efisien dan efektif dalam menghasilkan himpunan tricluster yang lebih optimal dan mempercepat waktu komputasi. Fuzzy Cuckoo Search adalah pengembangan dari Cuckoo Search yang menggunakan fungsi objektif Fuzzy C-Means untuk mengatasi ketidakjelasan (uncertainty) dalam data ekspresi gen. Analisis triclustering menggunakan metode gabungan Fuzzy Cuckoo Search berdasarkan Gaussian Distribution dengan -Trimax digunakan pada data ekspresi gen tiga dimensi sel fibroblas yang diberikan perlakuan dengan Egr-1 dan Tgf-, di mana ekspresi gen diamati pada 6 kondisi dan 2 titik waktu. Pada penelitian ini, himpunan tricluster yang memiliki kualitas terbaik berdasarkan Triclustering Quality Index adalah himpunan tricluster yang dihasilkan dengan nilai = 0,015 dan = 0,50 . Berdasarkan himpunan tricluster tersebut, didapatkan informasi penting mengenai kumpulan gen yang memiliki respon baik terhadap pemberian perlakuan dengan Egr-1, Tgf- dan bertahan setiap titik waktu. Kumpulan gen tersebut dilakukan Gene Ontology (GO) yang diuji menggunakan Fisher’s exact dengan tingkat signifikansi 0,05 dan dikoreksi dengan False Discovery Rate. Hasil GO tersebut terdiri dari 219 GO Terms Biological Process, 28 GO Terms Molecular Function, dan 52 GO Terms Cellular Component. GO Terms dari masing-masing aspek GO tersebut dapat dijadikan bahan untuk penelitian di bidang bioinformatika untuk menganalisis hubungan GO Terms terhadap penyakit Systemic Sclerosis (SSc).

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Triclustering analysis is one of the data mining methods aimed at clustering threedimensional data. Triclustering is commonly used in the field of bioinformatics to analyze the similarity of gene expression in an experiment at specific time points. The triclustering analysis in this research uses a combined method of Fuzzy Cuckoo Search based on Gaussian Distribution with -Trimax. This method combines the nodes deletion algorithm of -Trimax with the optimization algorithm of Fuzzy Cuckoo Search. The nodes deletion algorithm of -Trimax is used in the initial population formation phase of the tricluster. The concept of the nodes deletion algorithm is to produce tricluster sets with Mean Square Residue (MSR) below the threshold and close to 0. The optimization algorithm of Cuckoo Search is a search algorithm for tricluster solutions, depicted with the parasitism concept of cuckoo bird species. In this research, Cuckoo Search uses random walk Gaussian Distribution for tricluster solution search. This enhances the efficiency and effectiveness of the Cuckoo Search algorithm in producing more optimal tricluster sets and accelerating the computation time. Fuzzy Cuckoo Search is an extension of Cuckoo Search that employs Fuzzy C-Means objective function to handle uncertainty in gene expression data. The triclustering analysis using the combined method of Fuzzy Cuckoo Search based on Gaussian Distribution with -Trimax is applied to the three-dimensional gene expression data of fibroblast cells treated with Egr-1 and Tgf-1, where gene expressions are observed under 6 conditions and 2 time points. In this research, the tricluster set with the best quality based on the Triclustering Quality Index (TQI) is obtained with = 0.015 and = 0.50. Based on this tricluster set, important information is derived regarding groups of genes that respond well to treatment with Egr1, Tgf, and persist at each time point. These gene groups are subjected to Gene Ontology (GO) analysis, which is tested using Fisher's exact test with a significance level of 0.05 and corrected with False Discovery Rate. The GO results consist of 219 GO Terms Biological Process, 28 GO Terms Molecular Function, and 52 GO Terms Cellular Component. The GO Terms from each aspect can be utilized for further research in the field of bioinformatics to analyze the relationship of GO Terms with Systemic Sclerosis (SSc) disease."

"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. "

"Saat ini kompetisi di industri telekomunikasi semakin ketat. Perusahaan telekomunikasi yang dapat tetap menghasilkan banyak keuntungan yaitu perusahaan yang mampu menarik dan mempertahankan pelanggan di pasar yang sangat kompetitif dan semakin jenuh. Hal ini menyebabkan perubahan strategi banyak perusahaan telekomunikasi dari strategi 'growth '(ekspansi) menjadi 'value added services'. Oleh karena itu, program mempertahankan pelanggan ('customer retention') saat ini menjadi bagian penting dari strategi perusahaan telekomunikasi. Program tersebut diharapkan dapat menekan 'churn' 'rate 'atau tingkat perpindahan pelanggan ke layanan/produk yang disediakan oleh perusahaan kompetitor. Program mempertahankan pelanggan ('customer retention') tersebut tentunya juga diimplementasikan oleh PT Telekomunikasi Indonesia, Tbk (Telkom) sebagai perusahaan telekomunikasi terbesar di Indonesia. Program tersebut diterapkan pada berbagai produk Telkom, salah satunya Indihome yang merupakan 'home services' berbasis 'subscriber' berupa layanan internet, telepon, dan TV interaktif. Melalui kajian ini, penulis akan menganalisa penyebab 'churn' pelanggan potensial produk Indihome tersebut, sehingga Telkom dapat meminimalisir angka 'churn' dengan melakukan program 'customer retention' melalui 'caring' yang tepat. Mengingat ukuran 'database' pelanggan Indihome yang sangat besar, penulis akan menganalisis data pelanggan tersebut menggunakan metoda 'Big Data Analytics'. 'Big Data' merupakan salah satu metode pengelolaan data yang sangat besar dengan pemetaan dan 'processing' data. Melalui berbagai bentuk 'output', implementasi 'big data' pada perusahaan akan memberikan 'value' yang lebih baik dalam pengambilan keputusan berbasis data.

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Nowadays, telecommunication industry is very competitive. Telecommunication companies that can make a lot of profit is the one who can attract and retain customers in this highly competitive and increasingly saturated market. This causes change of the strategy of telecommunication companies from growth strategy toward value added services. Therefore, customer retention program is becoming very important in telecommunication companies strategy. This program hopefully can reduce churn rate or loss of potential customers due to the shift of customers to other similar products.

Customer retention program also implemented by PT Telekomunikasi Indonesia, Tbk (Telkom) as the leading telecommunication company in Indonesia. Customer retention program implemented for many Telkom products, including Indihome, a home services based on subscriber which provide internet, phone, and interactive TV. Through this study, the authors will analyze the cause of churn potential customers Indihome product, so that Telkom can minimize the churn number by doing customer retention program through the efficient caring.

Given by huge customer database the author will analyze using Big Data analytics method. Big Data is one method in data management that contain huge data, by mapping and data processing. Through various forms of output, big data implementation on the organization will provide better value in data-based decision making."

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ABSTRACTBig Data Analytics" will assist managers in providing an overview of the drivers for introducing big data technology into the organization and for understanding the types of business problems best suited to big data analytics solutions, understanding the value drivers and benefits, strategic planning, developing a pilot, and eventually planning to integrate back into production within the enterprise."

"In today’s fast growing digital world, the web, mobile, social networks and other digital platforms are producing enormous amounts of data that hold intelligence and valuable information. Correctly used it has the power to create sustainable value in different forms for businesses. The commonly used term for this data is Big Data, which includes structured, unstructured and hybrid structured data. However, Big Data is of limited value unless insightful information can be extracted from the sources of data.The solution is Big Data analytics, and how managers and executives can capture value from this vast resource of information and insights. This book develops a simple framework and a non-technical approach to help the reader understand, digest and analyze data, and produce meaningful analytics to make informed decisions. It will support value creation within businesses, from customer care to product innovation, from sales and marketing to operational performance.The authors provide multiple case studies on global industries and business units, chapter summaries and discussion questions for the reader to consider and explore. Big Data for Managers also presents small cases and challenges for the reader to work on – making this a thorough and practical guide for students and managers."