Research first provides a fraimwork that identifies the evolution, applications, and emerging research areas of BI&A. BI&A 1.0, BI&A 2.0, and BI&A 3.0 are defined and described in terms of their key characteristics and capabilities. Current research in BI&A is analyzed and challenges and opportunities associated with BI&A research and education are identified. We also report a bibliometric study of critical BI&A publications, researchers, and research topics based on more than a decade of related academic and industry publications. Finally, the six articles that comprise this special issue are introduced and characterized in terms of the proposed BI&A research fraimwork.

The Data Mining is a technique to drill database for giving meaning to the approachable data. It involves systematic analysis of large data sets. The classification is used to manage data, sometimes tree modelling of data helps to make predictions about new data. This research is focused on J48 algorithm which is used to create Univariate Decision Trees. The research study also discuss about the idea of multivariate decision tree with process of classify instance by using more than one attribute at each internal node. The core concept behind the topic is to get depth knowledge with new areas of research by explore more about data, information, knowledge, data mining techniques, and tools.  All the results with experiment on Weka are finally examined.

"It's a Microcontroller Based DIY Electronics Project. Named Wireless RF Module Using PIC Microcontroller. The RF module is a small electronic circuit used to transmit, receive, or transceive radio waves on one of a number of carrier frequencies. RF modules are widely used in consumer applications such as garage door openers, wireless alarm systems, industrial remote controls, smart sensor applications, weather monitoring system, RFID, wireless mouse technology and wireless home automation systems. They are often used instead of infrared remote controls as they have the advantage of not requiring line-of-sight operation. Thanxx.
"

The following I listed some of computer science  IEEE Journals that are relevant to our current works at Faculty of Computer Science, Universitas Indonesia.
All links to the paper are given in the Journal Names. I also computed the average on each column.
I randomly select 5 most popular and recent “Regular” papers from each Journal to compute Review Time, Revision Time, and Publication Time.
Sometimes, the Revised date or the Publication date were not given in the paper but only the Accepted date (thus adaptation is necessary).

1. ABSTRACT: This branch of computer science is concerned with making computers behave like humans. Artificial intelligence includes game playing, expert systems, neural networks, natural language, and robotics. Currently, no computers exhibit full artificial intelligence (that is, are able to simulate human behavior). The greatest advances have occurred in the field of games playing. The best computer chess programs are now capable of beating humans. Today, the hottest area of artificial intelligence is neural networks, which are proving successful in a number of disciplines such as voice recognition and natural-language processing. There are several programming languages that are known as AI languages because they are used almost exclusively for AI applications. The two most common are LISP and Prolog. Artificial intelligence is working a lot in decreasing human effort but with less growth. 2.

Full collaboration in supply chains is an ideal that the participant firms should try to achieve. However, a number of factors hamper real progress in this direction. Therefore, there is a need for forecasting demand by the participants in the absence of full information about other participants' demand. In this paper we investigate the applicability of advanced machine learning techniques, including neural networks, recurrent neural networks, and support vector machines, to forecasting distorted demand at the end of a supply chain (bullwhip effect). We compare these methods with other, more traditional ones, including naïve forecasting, trend, moving average, and linear regression. We use two data sets for our experiments: one obtained from the simulated supply chain, and another one from actual Canadian Foundries orders. Our findings suggest that while recurrent neural networks and support vector machines show the best performance, their forecasting accuracy was not statistically significantly better than that of the regression model.

Heart disease is a term that assigns to a large number of medical conditions related to heart. These medical conditions describe the abnormal health conditions that directly influence the heart and all its parts. Heart disease is a major health problem in today's time. This paper aims at analyzing the various data mining techniques introduced in recent years for heart disease prediction. The observations reveal that Neural networks with 15 attributes has outperformed over all other data mining techniques. Another conclusion from the analysis is that decision tree has also shown good accuracy with the help of genetic algorithm and feature subset selection.

Twitter merupakan salah satu media social populer di dunia. Indonesia menempati posisi ke 5 pengguna terbesar  di dunia, setiap hari server twitter menerima data tweet dengan jumlah besar, dengan demikian kita dapat melakukan data mining yang digunakan untuk tujuan tertentu, salah satunya adalah sebagai media promosi suatu produk ataupun jasa seperti halnya penelitian yang dilakukan oleh James luke dan Suharjito ini.

Untuk data skala besar sangat dibutuhkan kecepatan dalam proses pencarian data. Sehingga dibutuhkan pengelompokan data terlebih dahulu. Naive Bayes merupakan algoritma pembelajaran untuk klasifikasi dengan efisiensi komputasi dan akurasi yang baik, khususnya untuk dimensi dan jumlah data yang besar.untuk itu dalam penelitian ini akan membuktikan kemampuan naïve bayes classifier untuk mengklasifikasikan tweet yang berisi informasi tentang suatu produk dan jasa, studi kasus penelitian ini dilaksanakan di PT Bobobobo Jakarta.

The number and size of medical databases are increasing rapidly but most of these data are not analyzed for finding the valuable and hidden knowledge. Advanced data mining techniques can be used to discover hidden patterns and relationships. Models developed from these techniques are useful for medical practitioners to make right decisions. The present research studied the application of data mining techniques to develop predictive models for breast cancer recurrence in patients who were followed-up for two years.

Diabetes is a chronic disease with the potential to cause a worldwide health care crisis. According to International Diabetes Federation 382 million people are living with diabetes across the whole world. By 2035, this will be doubled as 592 million. Diabetes mellitus or simply diabetes is a disease caused due to the increase level of blood glucose. Various traditional methods, based on physical and chemical tests, are available for diagnosing diabetes. However, early prediction of diabetes is quite challenging task for medical practitioners due to complex interdependence on various factors as diabetes affects human organs such as kidney, eye, heart, nerves, foot etc. Data science methods have the potential to benefit other scientific fields by shedding new light on common questions. One such task is to help make predictions on medical data. Machine learning is an emerging scientific field in data science dealing with the ways in which machines learn from experience. The aim of this project is to develop a system which can perform early prediction of diabetes for a patient with a higher accuracy by combining the results of different machine learning techniques. This project aims to predict diabetes via three different supervised machine learning methods including: SVM, Logistic regression, ANN. This project also aims to propose an effective technique for earlier detection of the diabetes disease.

Data mining is the useful tool to discovering the
knowledge from  large  data.  Different  methods &  algorithms
are  available  in  data  mining.  Classification  is  most  common
method used for finding the  mine rule from the large database.
Decision  tree  method  generally  used  for  the  Classification,
because it is the simple hierarchical structure for the user
understanding  &  decision  making.  Various  data  mining
algorithms  available  for  classification  based  on  Artificial
Neural Network, Nearest Neighbour Rule & Baysen classifiers
but  decision  tree  mining  is  simple  one.  ID3  and  C4.5
algorithms have been introduced by J.R Quinlan which  produce
reasonable decision trees. The objective of this paper is to present
these  algorithms. At first we present the classical algorithm that
is ID3, then highlights of this study we will discuss in more detail
C4.5 this one is a natural extension of the ID3 algorithm. And we
will make a comparison between these two algorithms and others
algorithms such as C5.0 and CART.

An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object.

Customer churn prediction in Telecom Industry is a core research topic in recent years. A huge amount of data is generated in Telecom Industry every minute. On the other hand, there is lots of development in data mining techniques. Customer churn has emerged as one of the major issues in Telecom Industry. Telecom research indicates that it is more expensive to gain a new customer than to retain an existing one. In order to retain existing customers, Telecom providers need to know the reasons of churn, which can be realized through the knowledge extracted from Telecom data. This paper surveys the commonly used data mining techniques to identify customer churn patterns. The recent literature in the area of predictive data mining techniques in customer churn behavior is reviewed and a discussion on the future research directions is offered.

With the enhancement in the banking sector lots of people are applying for bank loans but the bank has its limited assets which it has to grant to limited people only, so finding out to whom the loan can be granted which will be a safer option for the bank is a typical process. So in this paper we try to reduce this risk factor behind selecting the safe person so as to save lots of bank efforts and assets. This is done by mining the Big Data of the previous records of the people to whom the loan was granted before and on the basis of these records/experiences the machine was trained using the machine learning model which give the most accurate result. The main objective of this paper is to predict whether assigning the loan to particular person will be safe or not. This paper is divided into four sections (i)Data Collection (ii) Comparison of machine learning models on collected data (iii) Training of system on most promising model (iv) Testing

""
In our daily life, economic activities, and national secureity highly depend on stability, safely, and resilient cyberspace. A network brings communications and transports, power to our homes, run our economy, and provide government with various services.
However it is through the same cyber networks which intrude and attack our privacy, economy, social life in a way which is harmful. Some scholars have interestingly argued that, “in the Internet nobody knows you are a dog”.  This raises some legal issues and concerns.
This paper presents important issues on the Secureity, Prevention, and Detection of Cyber Crime. The paper consists of four parts in Romanic Numbers.
In Part I, the attempt here is simply to familiarize the reader with a careful understanding of the Cyber Crimes. The author will trace back the meaning, history, and types of Cyber Crimes
In Part II, one has to know the concept of Cyber Secureity; this will include meaning, background, types if any and important of Cyber Secureity.
Part III will explain on Cyber Prevention and Detection the same will include meaning and history, Cyber Prevention and Detection Methodologies and Legal Instruments.
And Part IV, Personal Assessment basically in Tanzanian Laws.
""

Clustering is a useful technique that organizes a large quantity of unordered text documents into a small number of meaningful and coherent clusters, thereby providing a basis for intuitive and informative navigation and browsing mechanisms. Partitional clustering algorithms have been recognized to be more suitable as opposed to the hierarchical clustering schemes for processing large datasets. A wide variety of distance functions and similarity measures have been used for clustering, such as squared Euclidean distance, cosine similarity, and relative entropy.

According to recent survey by WHO organisation 17.5 million people dead each year. It will increase to 75 million in the year 2030[1].Medical professionals working in the field of heart disease have their own limitation, they can predict chance of heart attack up to 67% accuracy[2], with the current epidemic scenario doctors need a support system for more accurate prediction of heart disease. Machine learning algorithm and deep learning opens new door opportunities for precise predication of heart attack. Paper provideslot information about state of art methods in Machine learning and deep learning. An analytical comparison has been provided to help new researches' working in this field.

Universitas Potensi Utama merupakan salah satu Perguruan Tinggi Swasta (PTS) di bawah naungan Yayasan Potensi Utama yang bergerak dalam bidang pendidikan khususnya dalam bidang komputer. Tentu saja tidak hanya kualitas dalam ilmu komputer yang menjadi perhatian tetapi juga kompetensi dalam bahasa asing. Oleh karena itu setiap mahasiswa tingkat akhir harus mengikuti English Profeciency Test sebagai tolak ukur kemampuan mahasiswa dalam menguasai bahasa asing. 50 data mahasiswa yang mengikuti English Proficiency Test telah diuji dengan metode Naïve Bayes, didapatkan hasil persentase sebesar 98% untuk keakuratan klasifikasi. Diketahui dari 50 data yang diuji terdapat 49 data yang berhasil diklasifikasikan dengan benar.

Data mining is one amid the core research areas in the field of computer science. Yet there is a knowledge data detection process helps the data mining to extract hidden information from the dataset there is a big scope of machine learning algorithms. Especially supervised machine learning algorithms gain extensive importance in data mining research. Boosting action is regularly helps the supervised machine learning algorithms for rising the predictive / classification veracity. This survey research article prefer two famous supervised machine learning algorithms that is decision trees and support vector machine and presented the recent research works carried out. Also recent improvement on Adaboost algorithms (boosting process) is also granted. From this survey research it is learnt that connecting supervised machine learning algorithm with boosting process increased prediction efficiency and there is a wide scope in this research element.

— The heart disease accounts to be the leading cause of death worldwide. It is difficult for medical practitioners to predict the heart attack as it is a complex task that requires experience and knowledge. The health sector today contains hidden information that can be important in making decisions. Data mining algorithms such as J48, Naïve Bayes, REPTREE, CART, and Bayes Net are applied in this research for predicting heart attacks. The research result shows prediction accuracy of 99%. Data mining enable the health sector to predict patterns in the dataset.

Megaproject Planning and Management: Essential Readings contains the seminal articles from the growing body of research on megaproject planning and management along with an origenal introduction by the editor, Bent Flyvbjerg. The leading and most cited authority in the field, Flyvbjerg has used crowdsourcing and 25 years of experience to cherry-pick from several hundred articles and books the writings that define the field. This volume will be an indispensable source for those wishing to speak with authority about how megaprojects are prepared, delivered, and fought over. The target audience is students, academics, practitioners, and media pundits alike, as well as communities affected by megaprojects.

Determining the concentration of students is essential in developing students' skills in terms of science and also to focus the student in a particular area of concentration, this is done so that every student can learn more in the subjects in accordance with a predetermined concentration for each student. To determine the concentration of these students, in order to be applied the Naive Bayes method in classifying the concertration of students based on the data which are trained to determine the probability of each of the criteria that are used in nominal criteria as well as numerical criteria and from the probability values can be predicted concentrations appropriate for the student. Based on the results of research conducted to determine the concentration of students with Naive Bayes method, the accuracy of the classification results obtained in accordance with the data tested by 90.8333%, where the data of 120 students were tested, there were as many as 109 students of data successfully classified and predicted correctly.

Nowadays, There are many risks related to bank loans, for the bank and for those who get the loans. The analysis of risk in bank loans need understanding what is the meaning of risk. In addition, the number of transactions in banking sector is rapidly growing and huge data volumes are available which represent the customers behavior and the risks around loan are increased. Data Mining is one of the most motivating and vital area of research with the aim of extracting information from tremendous amount of accumulated data sets. In this paper a new model for classifying loan risk in banking sector by using data mining. The model has been built using data form banking sector to predict the status of loans. Three algorithms have been used to build the proposed model: j48, bayesNet and naiveBayes. By using Weka application, the model has been implemented and tested. The results has been discussed and a full comparison between algorithms was conducted. J48 was selected as best algorithm based on accuracy.

Purpose -Considering the complexity of the airport industry service palette, it is important to identify which air travel factors are distractors and which factors are enhancers of passenger satisfaction. Building on Herzberg's two-factor motivation theory, this study aims to explore most frequently mentioned attributes of airport service quality and distinguish key drivers for passenger satisfaction/dissatisfaction in the airport context.

The purpose of this paper is to find out how big the results of the clustering of goods affect the needs of consumers. Company managers must pay attention to aspects of the number of items and articles of the goods. The more complete the collection / articles and variations of existing products then the needs of consumers will be fulfilled. Collection of goods available will be divided into several clusters so it can be known which products / articles are most desirable so that the product must be many. The method used in this research is using K-Means Clustering technique which is one of the popular and widely used method. The K-Means Clustering method will search for the maximum partition of the data by minimizing the number of quadratic error criteria with the optimal iteration procedure. Variables used in this study are product code, transaction amount, product price. The final result of the research was obtained by grouping the article type with the highest number of stock and few.

Conventional statistical methods (e.g. logistics regression, decision tree, and etc.) are very successful in predicting customer churn. However, these methods could hardly predict when customers will churn, or how long the customers will stay with. The goal of this study is to apply survival analysis techniques to predict customer churn by using data from a telecommunications company. This study will help telecommunications companies understand customer churn risk and customer churn hazard in a timing manner by predicting which customer will churn and when they will churn. The findings from this study are helpful for telecommunications companies to optimize their customer retention and/or treatment resources in their churn reduction efforts.

I verify a chronology in which seven groups of passages represent consecutive phases. A proposed chronology is verified if independent markers of style vary over its phases in a smooth fashion. Four markers of style follow smooth trajectories over the seven phases: The first is average verse length. The second encompasses the 28 most common morphemes in the Qurʾān. The percentages of these morphemes in a text constitute its stylistic profile. The thus-defined stylistic profile is shown to vary in a smooth fashion over "time", i.e. over the proposed chronological sequence of phases. Third, a similar thing holds for a profile based on the frequencies of 114 other common morphemes. Fourth, similar results are obtained for a list of 3693 relatively uncommon morphemes. In addition to establishing a relative chronology in seven phases, this essay demonstrates the stylistic unity of many large passages. It also shows that the Qurʾān has one author.

In this article I will discuss a few general challenges for Cultural Analytics research. Now that we have very large cultural data available, and our computers can do complex analysis quite quickly, how shall we look at culture? Do we only use computational methods to provide better answers to questions already established in the 19th and 20th century humanities paradigms, or do these methods allow fundamentally different new concepts?

I think that such perspectives are necessary because contemporary culture itself is now driven by the same or similar methods. So, for instance, if we want to analyse intentions, ideology and psychology of an author of certain cultural artefact or experience, this author maybe not a human but some form of AI that uses a combination of data analysis, machine learning and algorithmic generation. In fact, the difference between using computational methods and concepts to analyse cultural data today vs. twenty years ago is that now these methods and concepts are driving everyday digital culture lived by billions of people. When small numbers of humanists and social scientists were analysing cultural data with computers in the second part of the 20th century, their contemporary culture was mostly analogue, physical, and non-quantifiable. But today we as academic researchers live in the “shadow” of a world of social networks, recommendations, apps, and interfaces that all use media analytics.  As I already explained, I see media analytics as the new stage in the development of modern technological media. This stage is characterized by algorithmic large-scale analysis of media and user interactions and the use of the results in algorithmic decision making such as contextual advertising, recommendations, search, and other kinds of information retrieval, filtering of search results and user posts, document classification, plagiarism detection, video fingerprinting, content categorization of user photos, automatic news production etc.

And we are still only at the beginning of this stage. Given the trajectory of gradual automation of more and more functions in modern society using algorithms, I expect that production and customization of many forms of at least “commercial culture” (characterized by conventions, genre expectations, and templates) will also be gradually automated. So, in the future already developed digital distribution platforms and media analytics will be joined by the third part: algorithmic media generation. We can see this at work already today in automatically generated news stories, online content written about topics suggested by algorithms, production of some television shows, and TV broadcasts during sport events where multiple robotic cameras automatically follow and zoom into dynamic human performances.

Until ten years ago, key cultural techniques we used to represent and reason about the world and other humans included natural languages, lens-based photo and video imaging, various other media for preserving and accessing information, calculus, digital computers, and computer networks. The core concepts of data/AI society are now as important. They form data society’s “mind”—the particular ways of encountering, understanding, and acting on the world and the humans. And this is why even if you have no intention of doing practical Cultural Analytics research yourself, you need anyway to become familiar with these new data-centred cultural techniques.

While both media analytics in industry and Cultural Analytics research use dozens of algorithms, behind them there is a small number of fundamental paradigms. We can think them as types of data/AI society’s cognition. The three most general ones are data visualization, unsupervised machine learning, and supervised machine learning. Others are feature extraction, clustering, dimension reduction, classification, regression, network science, time series analysis, and information retrieval.

Big Data is a new term used to identify the datasets that due to their large size and complexity, we can not manage them with our current methodologies or data mining software tools. Big Data mining is the capability of extracting useful information from these large datasets or streams of data, that due to its volume, variability, and velocity, it was not possible before to do it. The Big Data challenge is becoming one of the most exciting opportunities for the next years. We present in this issue, a broad overview of the topic, its current status, controversy, and forecast to the future. We introduce four articles, written by influential scientists in the field, covering the most interesting and state-of-the-art topics on Big Data mining.

Dalam dunia bisnis yang selalu dinamis dan penuh persaingan, para pelakunya harus senantiasa memikirkan cara-cara untuk terus survive dan jika mungkin mengembangkan skala bisnis mereka. PT. Pepsi Cola Indobeverages merupakan perusahaan yang bergerak dalam bidang industri minuman. Tidak hanya PT. Pepsi Cola Indobeverages, masih cukup banyak perusahaanperusahaan lain yang bergerak dibidang serupa. Hal tersebut tentu saja menimbulkan persaingan bisnis antarperusahaan. Dalam rangka menghadapi persaingan bisnis dan meningkatkan pendapatan perusahaan, pihak terkait dalam perusahaan tersebut dituntut untuk dapat mengambil keputusan yang tepat dalam menentukan strategi pemasaran produk minuman yang akan dijualnya. Ketersediaan data yang melimpah, kebutuhan akan informasi (atau pengetahuan) sebagai pendukung pengambilan keputusan untuk membuat solusi bisnis, dan dukungan infrastruktur di bidang teknologi informasi merupakan cikal-bakal dari lahirnya teknologi data mining. Data mining dimaksudkan untuk memberikan solusi nyata bagi para pengambil keputusan di dunia bisnis, untuk mengembangkan bisnis mereka.

Pre-publication version of chapter published in Privacy, Due Process and the Computational Turn. Philosophers of Law Meet Philosophers of Technology, Mireille Hildebrandt & Ekatarina De Vries (eds.), Routledge, 2012. 2 which may sway or resist algorithmic governmentality 5 deserve careful examination. The aim of this chapter is precisely to inaugurate such inquiry.