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Stunting is a serious public health problem, especially among under-fives, which can cause serious short- and long-term impacts. Efforts to tackle stunting in Indonesia involve national strategies and development priorities. Therefore, this study aims to compare the performance of machine learning regression algorithms in predicting stunting prevalence in Indonesia. The data collected is secondary data. The data collection was done carefully, taking explicit details regarding the source, scope, extent, and analysis of the dataset, and using a careful sampling methodology. The model evaluation results show that the Random Forest Regression algorithm has the best performance, with a success rate of 90.537%. The application of this model to the new dataset shows that East Nusa Tenggara province has the highest percentage of stunting at 31.85%, while Bali has the lowest percentage at 12.07%. Visualization of the dashboard using Tableau provides a clear picture of the distribution of stunting in Indonesia. In conclusion, this research contributes to the development of science, especially in the field of machine learning and public health, and provides policy recommendations for tackling stunting in Indonesia.

Exploratory data analysis (EDA), data visualization, and visual analytics are essential for understanding and analyzing complex datasets. In this project, we explored these techniques and their applications in data analytics. The case discusses Tableau, a powerful data visualization tool, and Google BigQuery, a cloud-based data warehouse that enables users to store, query, and analyze large datasets. It also explored the benefits and applications of both tools and their integration with other platforms and services. The project offers an introductory insight into Tableau's functionalities, employing a data file from the US Census Bureau via Google BigQuery. [ABSTRACT FROM AUTHOR]

The Titanic disaster is a topic that continues to fascinate. As the importance of analytics continues to grow in industry, data literacy skills have become increasingly important in business education. This project allows students to use the passenger data from the Titanic to build their data literacy skills using an engaging, experiential topic. The project requires students to extract, transform, describe, analyze, and draw conclusions regarding the factors that impacted survival on the Titanic. The project can be deployed using various application software and tools. We describe how the assignment can be completed using Excel, Tableau, and Python using the Pandas library. [ABSTRACT FROM AUTHOR]

This community engagement explores the application of Artificial Intelligence (AI) and data visualization in the context of education through an interactive workshop. The workshop aims to enhance the capacity of high school teachers in Jakarta to utilize technology for data analysis using applications like Tableau. Participants engage in hands-on practice with four case studies: analysis of student performance by gender, the influence of parental education on student outcomes, evaluation of the effectiveness of exam preparation courses, and analysis of the impact of lunch programs on student achievement. Results indicate a significant improvement in participants' understanding and skills in using data to support evidence-based decision-making in educational settings.

This case builds students' critical thinking skills and knowledge of how Big Data analytics are used in an audit. Using real local government data, students will utilize Tableau to create and analyze visualizations to answer questions regarding governmental entities' fiscal health, search for anomalies, and make recommendations to management. These skills are increasingly necessary in today's workplace and will also be more heavily tested on the CPA Exam beginning in 2024. Additionally, this case addresses AACSB Business Accreditation Standard 4, which calls for critical thinking and current, forward-looking curriculum, and AACSB Accounting Accreditation Standard 5, which calls for the integration of information technology into the curriculum and preparing students to adapt to emerging technologies. Evidence suggests this case improves students' confidence with Big Data analytics and is a useful learning tool. The case is appropriate for use in data analytics, accounting information systems, governmental accounting, and auditing classes. Data Availability: Efficacy data are available from the authors. [ABSTRACT FROM AUTHOR]

This case asks students to assume the role of a newly hired (data) analyst for Divvy, Chicago's bike share program. In this role, they use data analytics software to perform data analysis and data visualization techniques. This case can be implemented in multiple parts, and it is scalable for the unique needs of instructors and their students. Introductory topics include assessing the properties of the data, describing data validation techniques, creating calculated variables, and constructing basic data visualizations. Advanced topics include joining additional data sources, developing and investigating research questions, and creating dashboards to tell a meaningful story about the data. The case is unique because it enables students to interact with a large, real-world dataset, and it is ideal for accounting information systems or data analytics courses. [ABSTRACT FROM AUTHOR]

The safety-related raw water system's strong operational condition supports the radiation defense and biological shield of nuclear plant containment structures. Gaps and failures in maintaining proper working condition of main equipment like pump were among the most common causes of unavailability of safety related raw water systems. We integrated the advanced data analytics tools to evaluate the maintenance records of water systems and gave special consideration to deficiencies related to pump. We utilized maintenance data over a three-and-a-half-year period to produce metrics like MTBF, MTTF, MTTR, and failure rate. The visual analytic platform using tableau identified the efficacy of maintenance & deficiency in the safety raw water systems. When the number of water quality violation was compared to the other O&M deficiencies, it was discovered that water quality violations account for roughly 15% of the system's deficiencies. The pumps were substantial contributors to the deficit. Pump availability was predicted and optimized with real time data using response surface method. The prediction model was significant with r-squared value of 0.98. This prediction model can be used to predict forth coming pump failures in nuclear plant.

Copyright of XVIIe Siècle is the property of Presses Universitaires de France and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We give representation theoretic interpretation for the terms in Wilson's rank formula (1.2) for the inclusion matrices of complete uniform families. We exhibit a dual Specht filtration of a suitable adjoint Radon map whose factors contribute the terms in the rank formula. From a representation theoretic point of view we give descriptions for the (complete) kernel and image of these maps in the case of partitions with at most two parts. The primary tools are the Specht filtration given by James in [10], and, in the dual setting, the work on polynomial functions by Friedl and Rónyai [6]. [ABSTRACT FROM AUTHOR]

Copyright of Arab International Journal of Information Technology & Data is the property of Arab Institution of Knowledge Management and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This research discusses the implementation of Business Intelligence (BI) to enhance the profitability of The Look Company through the synergy of product departments. In the era of big data, a company's ability to process and analyse data becomes a crucial factor in achieving competitive advantage. This research aims to present data information from The Look Company's Product Departments in the form of visualizations, graphs, and diagrams that can be used as indicators for decision-making. It also aims to identify products that are not generating significant profitability for the company in order to optimize production costs and to identify products that experience frequent cancellations and returns. This analysis and recommendations are intended to assist The Look Company in its decision-making process through the implementation of a comprehensive and detailed Business Intelligence study. Additionally, this research outlines the steps of data collection and processing methods using tools such as Google Colab, Postgree, and Tableau to generate visualizations, graphs, and diagrams that provide indicators and recommendations for decision-making within The Look Company. The research results indicate that the BI data visualization dashboard plays a crucial role in determining business strategies and providing rapid and accurate decision-making recommendations. [ABSTRACT FROM AUTHOR]

Purpose: Global discussion issues include managing innovation and incorporating digitalization into higher education. Combining and balancing these, digitalization may hold the key to enhancing higher education's capacity for innovation and expanding the use of cutting-edge learning technologies into their curricula, ultimately boosting student achievement. The distinctiveness of the research is on the need to improve higher education's administration, instruction, and practice via the process of innovation and digitalization of higher education. The primary goal research purpose of the study is to examine the relationships between higher education and different facets of digitalization in the context of European countries. Design/methodology/approach: Methods used to carry out the empirical analysis were EViews 12.0, SPSS 28, and Tableau. Moreover to find out whether there is a connection between digitalization and higher education, panel regression and Granger causality were applied. Due to data accessibility, we utilized data from 31 European nations for the 2013 and 2020 empirical research relating the Digital Economy and Society Index (DESI) components and higher education. Findings: The results suggest that every hypothesis was correct, and digitalization is crucial for higher education since it shows outstanding levels of dependability with Industry 4.0. The integration of digital technologies, internet usage, and digital public services all have a significant influence on higher education in EU nations. Additionally, studies have shown that throughout time, the higher education systems in various European nations have changed in diverse ways in terms of digitalization. As a result, the integration of higher education and innovation on a new digital foundation will support digital public services of research discoveries and creative operations of higher education institutions. Originality/value: The challenges of the human capital required in the digital economy have received the bulk of attention in research on innovation and digitalization in higher education. In the case of European countries, there are no empirical research on the connections between elements related to digitalization and higher education. This document fills the gap in this situation. The novel of the study tackles digitalization in higher education and the need of enhancing managers, educators, and practitioners' professional growth in higher education via the process of innovation. [ABSTRACT FROM AUTHOR]

Students' performance analysis basically consists of determining the factors influencing the performance and how it will give impact towards success. It will help us to understand students' behavior and how to improve their academic performance. The efficiency of this analysis depends on the information given by the user through learning management system (LMS). In order to improve the information, we have applied algorithms on the dataset and prepared a model by using Tableau and RapidMiner. Cross-validation with decision tree also has been applied on datasets. This can help in evaluating statistical computational results into a generalized data set. Based on the calculation of data mining, it can analyze that our model is quite stable since it has high accuracy with lower standard deviation. So, the processes like testing and validation, applying the model and decision tree on RapidMiner generates the output in a specific form. The result shows that the percentage of students who are absence is better than students who are absence more than 7 days. At last, a model is prepared, and it can help the schools, students, and the parents in adapting appropriate measures to ensure the success of students at school. [ABSTRACT FROM AUTHOR]

Data literacy has become a much sought after skill by organizations as the importance of data driven decision making continues to rise in importance. This study explores the teaching of Tableau as a supplement to Excel to enhance the data literacy skills of students. Twenty-eight students taking an introductory course in Business Analytics and Information Systems completed a series of five tasks in both Excel and Tableau and answered a two question survey on their perceptions of the relative ease of use and ability to produce correct and visually pleasing results for Tableau versus Excel. An assessment of the results showed that Tableau submissions produced higher scores in terms of correctness and more professionally formatted visualizations. Student survey responses also supported a significant preference for Tableau over Excel in terms of both ease of use and ability to produce correct results. [ABSTRACT FROM AUTHOR]

Big data analytics, described as the fourth paradigm of science breaking through Industry 4.0 technological development, continues to expand globally as organizations strive to attain the utmost value and sustainable competitive edge. Yet, concerning its contribution to developing sustainable products, there is a need for innovative research due to limited knowledge and uncertainty. This research is hence aimed at addressing (a) how research on big data analytics for sustainable products has evolved in recent years, and (b) how and in what terms it can contribute to developing sustainable products. To do so, this study includes a bibliometric review performed to shed light on the phenomenon gaining prominence. Next, the fuzzy technique for order of preference by similarity to ideal solution, along with a survey, is used to analyze the matter in terms of the respective indicator set. The review's findings revealed that there has been growing global research interest in the topic in the literature since its inception, and by advancing knowledge in the area, progress toward sustainable development goals 7, 8, 9, 12, and 17 can be made. The fuzzy-based analytical findings demonstrated that 'product end-of-life management efficiency' has the highest contributory coefficient of 0.787, followed by 'product quality and durability' and 'functional performance', with coefficients of 0.579 and 0.523, respectively. Such research, which is crucial for sustainable development, offers valuable insights to stakeholders seeking a deeper understanding of big data analytics and its contribution to developing sustainable products. [ABSTRACT FROM AUTHOR]

Due to the increasingly digital nature of library resources and collections, it is sometimes difficult to envision a library’s unified holdings and to understand how they have changed over time. Conducting a collection analysis and applying data visualization techniques can be an excellent way to get a top-down view of the collection as a whole. This article outlines the author’s process for a collection analysis of the Weinberg Memorial Library’s entire catalog of print and electronic resources. It explores the rationale behind some key collection analysis decisions and discusses approaches for data extraction and clean-up as well as visualization using the Tableau software.

Copyright of Kriminologisches Journal is the property of Julius Beltz GmbH & Co. KG Beltz Juventa and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Recherches en Langue et Littérature Françaises is the property of University of Tabriz and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Customer churn is a well-known issue in most sectors, there are various reasons why customers leave banks such as customers aren't getting the results they want, customer service needs improvement, customers believe your competitors can do a better job, customers no longer see the value in bank's product, and customers believe your product is too expensive or too cheap. Hence it's critical to develop a perfect predictive model designed in support of customer churn that could be used to formulate a customer retention strategy. This topic is much more important in markets where competition is high and acquiring new customers is more difficult than retaining existing customers. There is a need to build a Bank customer churn/no churn analysis system with best performance and accuracy that will be of great use in banking enabling them to prevent revenue loss, reduce marketing and sales costs and improve the quality of customer service. Coupled with Business Intelligence (BI) tools like Tableau, business executives can make sense of big data. The research focuses on using the machine learning model random forest to help the machine learning model evaluation and interpretability for customer churn analysis in having bank account and are associated with the credit card services, even though multiple models are employed for this analysis. [ABSTRACT FROM AUTHOR]

This story of the life paths of the teachers and students of the seminary is told on the trail of a photograph, that is, a tableau of the graduates of the Serbian Orthodox Theological-Teaching School in Prizren, in 1896. It is about their contribution to education, church, Christianity, theology, literature, ethnology, and science. Like other photographs created in studios throughout the Serbian cultural space, they are more than documents, they are an inexhaustible treasure for historians and other cultural workers. They visually remind us of our ancestors and tell us about a time that no longer exists, the time of nobility. Professors and graduates are dressed according to the European style of clothing, unusual for those occasions and that time, when professors had to wear a fez, so as not to be distinguished from citizens of other religions. The place and time of the photograph is the 19th century, the Ottoman Empire, Kosovo Vilayet, the 'Dark Vilayet'! An authentic 1896 photo from Prizren is a true rarity. There is no information on taking photos in the archival material of the Theological School, but one thing is certain, the photography was created in Prizren that year, but the name of the photographer is unknown. There are 27 people on the tableau, rector Ilarion (Ivan) Vesić, eight teachers, Trivun Dimitrijević, Kosta Lozanić, Mihailo S. Tasić, Milan Gajić, Sava Jakić, Danilo (Nedeljković), Vasilije Stojanović, Apostol Filipovic, and 18 graduates, Toma Burković, Ljubomir A. Bulatović Ibrijski, Milutin V. Vukić, Janićije (Naća) Živković, Janko Đorđević, Haralampije Ilić, Pantelija Mandić, Dusan Nekic, Milan Nikšić, Radivoje R. Orlović, Atanasije Petrović, Blagoje Petrović, Obrad Popović, Nikola Rožić, Mladen Stojanović, Petar Ćićarić, Vladimir Popović and Zdravko Tasić. This is the first generation of high school graduates who completed the six-year Serbian Orthodox Theological-Teaching School. The Council of the Theological School declared them capable of being teachers and they were issued with certificates of teacher training. When they finished their education, they were between 17 and 26 years old. Except for one student who continued his education at the Belgrade Theological School, all the others were appointed as teachers. Obviously, the need for trained teachers throughout Old Serbia was great that year. In further service, five students were ordained as priests, one teacher became a merchant. Only one former student finished the Spiritual Academy, and two continued their education after the Theological School. Of the 18 students, five were from Prizren, three from the surrounding area. For the first time, a student from Bosnia attended the Theological School. Four young men were from Macedonia, two from Montenegro, two from the Kingdom of Serbia, and one from Kosovo. Of the nine teachers, four had university degrees, two were monks, which was a requirement for the Theological School. In different years, they were able to make contact with the students and help them not only as teachers, but also as guardians, because it was a boarding school. The goal of the Theological School in Prizren was to educate and form an educated staff of teachers and priests, which it successfully realized. Teachers and priests in Old Serbia were, in addition to their vocation, a kind of national workers and cultural activists because the Theological School moulded them as patriots.

This study aims to determine the dominant chemical elements that may improve the monitoring of the productivity and efficiency of heavy engines in 2015-2021 in the company. The method used is usually Scheduled Oil Sampling. This article proposes a new approach. The research problems are analyzing the recorded chemical elements that are produced by heavy engines and visualizing them through the Tableau program. The basic design of the study is learning the given data after visualization and using the Principal Component Analysis. This method is to obtain chemical elements that affect engine wear during each engine's use in the 2015-2021 period. Because there are three categories in each element in the oil sample, namely wear metals, contaminants, and oil additives, a technique is needed to obtain these elements using Principal Component Analysis. Therefore, Oil Sampling Analysis through data exploration using Tableau resulted in a new approach to data analysis of elements recorded by heavy vehicles. The main findings as a result of the analysis are given by the visualization of Tableau, in which there are five machines analyzed to obtain the main components that cause engine wear. From the visualization results, it is shown that there is one engine coded MSD 012 that experienced wear and tear in 2018 and 2019. This shows where two main components, Ca and Mg, dominate engine wear. These results have been confirmed with the related companies. The company then carried out further studies on the machine to get special treatment because of these results.

Learning history has a central role in cultivating patriotism and nationalism value for students. Various countries have placed historical education as a crucial part of national education. It is due to the belief that historical education materials can develop the nature and character of the nation's young generation. Technological developments make it easier for us to create innovative media, such as web scraping. Learning history with proper media can foster students' motivation to understand the content, such as the names and roles of national heroes. Web scraping is used to collect data from sites related to 60 national heroes and their places of birth. The data is then processed and visualized in Tableau Public. The result shows that the learning media can be categorized as good (53%). The respondent stated that the history learning media is very useful (67%). Meanwhile, regarding the effectiveness of the linkage feature between the data in the visualization, all respondents stated is good.

According to the administration of research data and funding for governmental organizations and funding agencies, the National Research Council of Thailand (NRCT) employs a National Research Management System (NRMS). NRCT needs different categories of data to provide information to various government departments to make policy decisions related to research funding. NRMS has so far collected a significant amount of such data. However, the tools needed for prompt analysis of such large amounts of data are inadequate. The purpose of this article is to discuss a multi-dimensional report system to provide enhanced information to support the decisions of policymakers. This involves creating reports with diverse views based on the numerous fields in the system, such as sorting by year, funding entity, ministry, department, policy and research strategy, and research issues, etc. and exhibiting a dashboard report to the officers and executives responsible for decision-making in the form of an overview of national research data by using Business Intelligence (BI). For the development of the report system, the research uses Tableau software. The officers who assessed the proposed report system expressed satisfaction, as shown by the outcomes of the user evaluations. A key feature mentioned was the ability to submit more reports to high-level administrators quicker.https://dorl.net/dor/20.1001.1.20088302.2022.20.4.2.1

Abstract Virtual machines (VMs) are preferred by the majority of organizations due to their high performance. VMs allow for reduced overhead with multiple systems running from the same console at the same time. A physical server is a bare-metal system whose hardware is controlled by the host operating system. A physical server runs on a single instance of OS and application. A virtual server or virtual machine encapsulates the underlying hardware and networking resources. With the existing physical server, it is difficult to migrate the tasks from one platform to another platform or to a datacentre. Centralized security is difficult to setup. But with Hypervisor the virtual machine can be deployed, for instance, with automation. Virtualization cost increases as well as a decrease in hardware and infrastructure space costs. We propose an efficient Azure cloud framework for the utilization of physical server resources at remote VM servers. The proposed framework is implemented in two phases first by integrating physical servers into virtual ones by creating virtual machines, and then by integrating virtual servers into cloud service providers in a cost-effective manner. We create a virtual network in the Azure datacenter using the local host physical server to set up the various virtual machines. Two virtual machine instances, VM1 and VM2, are created using Microsoft Hyper-V with the server Windows 2016 R. The desktop application is deployed and VM performance is monitored using the PowerShell script. Tableau is used to evaluate the physical server functionality of the worksheet for the deployed application. The proposed Physical to Virtual to Cloud model (P2V2C) model is being tested, and the performance result shows that P2V2C migration is more successful in dynamic provisioning than direct migration to cloud platform infrastructure. The research work was carried out in a secure way through the migration process from P2V2C.

The 2020 Census County Assessment Tool was developed to assist decennial census data users in identifying deviations between expected census counts and the released counts across population and housing indicators. The tool also offers contextual data for each county on factors which could have contributed to census collection issues, such as self-response rates and COVID-19 infection rates. The tool compiles this information into a downloadable report and includes additional local data sources relevant to the data collection process and experts to seek more assistance. [ABSTRACT FROM AUTHOR]