Perbandingan Algoritma Klasifikasi Data Mining Model C4.5 dan Naive Bayes untuk Prediksi Penyakit Diabetes

—Diabetes is one of the deadly disease, high risk factors in families that cause diabetes because fat people who do not do physical exercise, and those who do not have a healthy lifestyle and diet excess of what is needed by the body. Based on the history data diabetics can be made on the prediction of diabetes that can help health professionals. Classification is one of data mining techniques that can be used to help predict .Classification can be done with that Decision Tree algorithm C4.5 and Naive Bayes. This study aims to classify and apply data mining classification. Results of data classification in the evaluation using the Confusion Matrix and ROC curve to determine the level of accuracy results using algorithms Decision Tree that is equal to 73.30% and the AUC of the ROC curve was 0733 while the algorithm Naive Bayes amounted to 75.13% AUC values of the ROC curve of 0.810, so it can be said that the algorithm Naive Bayes have the result of a good predictor in predicting diabetes patient

Perbandingan Algoritma Klasifikasi Data Mining Model C4.5 dan Naive Bayes untuk Prediksi Penyakit Diabetes

—Diabetes is one of the deadly disease, high risk factors in families that cause diabetes because fat people who do not do physical exercise, and those who do not have a healthy lifestyle and diet excess of what is needed by the body. Based on the history data diabetics can be made on the prediction of diabetes that can help health professionals. Classification is one of data mining techniques that can be used to help predict .Classification can be done with that Decision Tree algorithm C4.5 and Naive Bayes. This study aims to classify and apply data mining classification. Results of data classification in the evaluation using the Confusion Matrix and ROC curve to determine the level of accuracy results using algorithms Decision Tree that is equal to 73.30% and the AUC of the ROC curve was 0733 while the algorithm Naive Bayes amounted to 75.13% AUC values of the ROC curve of 0.810, so it can be said that the algorithm Naive Bayes have the result of a good predictor in predicting diabetes patient

The Hound of the Baskervilles: Annotated with Reading Strategies

Welcome to the reading strategy enhanced version of Sir Arthur Conan Doyle’s book, The Hound of the Baskervilles, this book has been redesigned to help you with this famous fictional work. If you have been having trouble understanding what is going on when you read a book, then it is important to change the way you read a book. This book should help you practice with a number of strategies as you read with purpose and become an active reader. To read with a purpose you will have things to be thinking about as you begin to read a chapter and activities to do to help you better understand what you have read. Put together, these activities are useful in helping you practice, access, and organize information and better understanding your reading. When you are Reading with Purpose, that means doing more than just reading the words in a chapter or section and hoping that you understand or remember it, but instead you start by thinking about what and why you are reading, even before you start reading. You might be thinking about what you already know about the book, predicting what you think a chapter is about, or looking for specific things like setting elements of where and when the story is taking place. The ideas here are to make your reading more active by you doing things about what you are reading. These activities help give you a purpose in your reading. For example let\u27s start with why you are reading this book. I’m reading The Hound of the Baskervilles..... to practice my reading to get a good grade in my class to pass the test about the book to learn about gardening for my own pleasure I heard about the story and it sounded fun because it looked interesting because I’m bored Did any of those reasons fit for why you are about to begin reading this book? If so then you have identified your reading purpose for this book and have done a reading strategy - you have stated your purpose in reading this book.https://digitalcommons.unf.edu/secondary_resources/1001/thumbnail.jp

Digital information and the 'privatisation of knowledge'

Purpose of this paper: To point out that past models of information ownership may not carry over to the age of digital information. The fact that public ownership of information (for example, by means of national and public library collections) created social benefits in the past does not mean that a greater degree of private sector involvement in information provision in the knowledge society of today is synonymous with an abandonment of past ideals of social information provision. Design/methodology/approach: A brief review of recent issues in digital preservation and national electronic heritage management, with an examination of the public/private sector characteristics of each issue. Findings: Private companies and philanthropic endeavours focussing on the business of digital information provision have done some things - which in the past we have associated with the public domain - remarkably well. It is probably fair to say that this has occurred against the pattern of expectation of the library profession. Research limitations/Implications:The premise of this paper is that LIS research aimed at predicting future patterns of problem solving in information work should avoid the narrow use of patterns of public-private relationships inherited from a previous, print-based information order. Practical implications: This paper suggests practical ways in which the library and information profession can improve digital library services by looking to form creative partnerships with private sector problem solvers. What is original/value of the paper? This paper argues that the LIS profession should not take a doctrinaire approach to commercial company involvement in 'our' information world. Librarians should facilitate collaboration between all parties, both public and private, to create original solutions to contemporary information provision problems. In this way we can help create pragmatic, non-doctrinaire solutions that really do work for the citizens of our contemporary information society

Waters that matter:How human-environment relations are changing in high-Arctic Svalbard

There is scientific consensus that the archipelago of Svalbard is warming up faster than other parts of the planet. People who live in or regularly visit this part of the European high Arctic observe and experience these changes in a subjective and relational manner. This article illustrates how perceptions of environmental change are enmeshed with our ways of interacting with water(s) and dwelling in the landscape. What kind of water-related change do people talk about? How do changes in the different water worlds matter? How does water help us portray what environmental change means? We show that “what” and “how” we know about water(s) amidst change are in many ways inseparable. Our contribution offers a benchmark for discussing water-related environmental change in Svalbard from a perspective that goes beyond “what long-term monitoring tells us” towards “what bodies experience.” Through accounts shared mostly by scientists, technicians, and tour guides, we explore notions of water in its various forms, such as sea ice, glaciers, rivers, the wetness of the tundra, snow, and weather phenomena including rain. We focus on processes such as disappearing, melting, freezing, swelling, saturating, drying up, eroding, appearing, and threatening, and discuss what the observed and experienced changes mean for human-environment relations. Our interlocutors emphasize many facets of their relationship with the landscape, including identity, expectations, emotions, knowledge, and practices. Our study demonstrates how the experiential perspective is largely ordered and filtered through activities and practices, among which mobility and reading, or predicting, the landscape stand out as particularly important. Through a relational approach to water(s) permeation, we apply Tim Ingold’s concept of taskscapes and his perspectives on dwelling to show how time scales and connection to place matter. We juxtapose scientific knowledge produced through long-term monitoring with experiential knowledge, and demonstrate their entanglement in the Svalbard context, dominated by scientific ways of knowing

Prediction Sequence Patterns of Tourist from the Tourism Website by Hybrid Deep Learning Techniques

Tourism is an important industry that generates incomes and jobs in the country where this industry contributes considerably to GDP. Before traveling, tourists usually need to plan an itinerary listing a sequence of where to visit and what to do. To help plan, tourists usually gather information by reading blogs and boards where visitors who have previously traveled posted about traveling places and activities. Text from traveling posts can infer travel itinerary and sequences of places to visit and activities to experience. This research aims to analyze text postings using 21 deep learning techniques to learn sequential patterns of places and activities. The three main techniques are Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU) and a combination of these techniques including their adaptation with batch normalization. The output is sequential patterns for predicting places or activities that tourists are likely to go and plan to do. The results are evaluated using mean absolute error (MAE) and mean squared error (MSE) loss metrics. Moreover, the predicted sequences of places and activities are further assessed using a sequence alignment method called the Needleman–Wunsch algorithm (NW), which is a popular method to estimate sequence matching between two sequences

Predicting Potential For Promotion: How The Data In Human Resource Information Systems Can Be Used To Help Organizations Gain Competitive Advantage

This paper utilizes the data contained in the Human Resources Information System (HRIS) of a company, called here “Engineering Solutions,” and analyzes the drivers of potential for promotion among a sample of engineers. The methods used consist of basic statistical procedures, multiple regressions, ordered logits, and decompositions. The results show which variables are the main drivers of potential for promotion in this organization, which are minor drivers, and which do not matter at all

Methods for Analyzing Attribute-Level Best-Worst Discrete Choice Experiments

Discrete choice experiments (DCEs) have applications in many areas such as social sciences, economics, transportation research, health systems, and clinical decisions to mention a few. Usually discrete choice models (DCMs) focus on predicting the product choice; however, these models do not provide information about what attributes of the products are impacting consumers’ choices the most. Today, it is common to record the best and worst features of a product (or profile), also called attribute levels, and the goal is to investigate and build models for estimation of attribute and attribute-level impacts on consumer behavior. Attribute-level best-worst DCEs provide information into what consumers find the most important when considering different products. The design of attribute-level best-worst DCEs and the associated theory are discussed by Street and Knox (2012). Attribute-level best-worst discrete choice models can help to market products to the consumers and are often used in health economics research. These experiments help companies to best target consumers with their products or services. The latter can better advertise their products by highlighting and/or downplaying certain key attributes (or attribute-levels) to best earn the interests and business of consumers. We propose a time dependent model that can adapt to changes that occur in areas such as public opinion. A time dependent model accounts for the impact of time in as consumer’s perception of a product and adjusts the utility to reflect that. These models are Markov processes and are often found under dynamic programming. Rust (1994), provides time dependent models for the usual DCEs. We extend this time dependent model to the attribute-level best-worst DCEs. Two example studies are presented to examine the dynamic versus static performance of transition matrices for estimation and inference of attributes and attribute level effects with regards to the expected utilities

Predicting Motivations of Actions by Leveraging Text

Understanding human actions is a key problem in computer vision. However, recognizing actions is only the first step of understanding what a person is doing. In this paper, we introduce the problem of predicting why a person has performed an action in images. This problem has many applications in human activity understanding, such as anticipating or explaining an action. To study this problem, we introduce a new dataset of people performing actions annotated with likely motivations. However, the information in an image alone may not be sufficient to automatically solve this task. Since humans can rely on their lifetime of experiences to infer motivation, we propose to give computer vision systems access to some of these experiences by using recently developed natural language models to mine knowledge stored in massive amounts of text. While we are still far away from fully understanding motivation, our results suggest that transferring knowledge from language into vision can help machines understand why people in images might be performing an action.Comment: CVPR 201