Data Analysis Tools Research Articles

Pemanfaatan Teknologi Marketing Dan Inovasi Produk Terhadap Keunggulan Bersaing Untuk Meningkatkan Kinerja Pemasaran

This study aims to empirically prove the effect of using social media and product innovation on competitive advantage to improve marketing performance. This research is a quantitative research with data analysis tools used Structural Equation Modeling (SEM) in the AMOS 2.6 program. The results of this study prove and conclude that marketing technology and product innovation have an effect on competitive advantage, and competitive advantage has an effect on marketing performance. Theoretically, this study confirms that there is a positive relationship between factors that influence competitive advantage, namely the use of marketing technology and product innovation, as well as a positive relationship between competitive advantage and marketing performance, where the findings in this study support many similar research results.Competitive advantage plays a role in improving marketing performance, to increase competitive advantage it is necessary to utilize marketing technology and innovate products.

Analysis of Large Data Sets in a Physical Chemistry Laboratory NMR Experiment Using Python.

We describe an update to an experiment demonstrating low-field NMR spectroscopy in the undergraduate physical chemistry laboratory. A Python-based data processing and analysis protocol is developed for this experiment. The Python language is used in fillable worksheets in the notebook software JupyterLab, providing an interactive means for students to work with the measured data step by step. The protocol teaches methods for the analysis of large data sets in science or engineering, a topic that is absent from traditional chemistry curricula. Python is among the most widely used modern tools for data analysis. In addition, its open-source nature reduces the barriers for adoption in an educational laboratory.

PolArStat: An Arduino based potentiostat for low-power electrochemical applications

A potentiostatic circuit for low-power applications in the range of ±3.3V and ±13.75mA is developed. The device achieves a full-scale resolution of ≈1.6mV and ≈1μA — sufficient for a wide range of electroanalytical experiments. For controlling the potentiostat a Python-based graphical user interface (GUI) is developed and implemented in our open-source electrochemistry software Polarographica. This enables electroanalytical experiments such as cyclic voltammetry (CV) and chronoamperometry (CA) in a live-graphing environment at an operational convenience similar to commercial devices, combined with a powerful tool for subsequent data analysis. The performance of our potentiostat is validated by the excellent agreement of CV and CA data acquired with five different in-house built devices and a commercial Gamry Reference 620. Finally, we provide a printed circuit board (PCB) design of our device as an Arduino shield that requires only a minimum of additional components. This powerful open-source tool is suitable for customized electrochemical experiments at material costs of less than 40 €.

New heteroscedasticity-adjusted ridge estimators in linear regression model

In ridge regression, we are often concerned with acquiring ridge estimators that lead to the smallest mean square error (MSE). In this article, we have considered the problem of ridge estimation in the presence of multicollinearity and heteroscedasticity. We have introduced a scaling factor which leads to significantly improved performance of the ridge estimators as compared to their classical counterparts. For illustration purposes, we have applied our proposed methodology to some of the popular existing ridge estimators but it can be extended to other estimators as well. We have also compared our proposed estimator with popular existing estimators dealing estimation problem in the same scenario. Extensive simulations reveal the suitability of the proposed strategy, particularly in the presence of severe multicollinearity and heteroscedasticity. A real-life application highlights that the proposed strategy has the potential to be a useful tool for data analysis in the case of collinear predictors and heteroscedastic errors.

Development of integration and data analysis subsystems of information and analytical platform for support and decision-making in the field of regional environmental policy

The paper presents the results of research conducted within the framework of the program-targeted financing project BR18574148 of the Ministry of Science and Higher Education of the Republic of Kazakhstan, aimed at developing a unified information and analytical platform ecomodeling.kz to support and make decisions in the field of regional environmental policy. This paper describes three subsystems of this platform that integrate the platform with various information sources, analyze the monitoring data of the atmosphere of industrial cities in Kazakhstan, predict the values of the concentration of harmful substances using machine learning models and display the results. The article describes the technical aspects of obtaining measurement data from two information sources, the structure of the database, the developed tools for data analysis, the machine learning models used, and the user interface of the web application.

GUI-Based Data Visualisation Tool for Option C IPMVP using Jupyter Notebook

IPMVP is an acronym for International Performance Measurement and Verification Protocol, used for calculating energy savings by taking some external factors into account to give an equal base for a linear comparison between two periods. However, determining the significant independent variable for routine adjustment causes inconvenient repeated action of regression analysis modeling, which leads to the exhaustive calculation and human error risk. These exasperating actions led to the creation of this project, which aims to provide a user-friendly, insightful, and accurate energy-saving calculation program tool. Thus, the main objective of this project is to develop an energy-saving computational tool for option C IPMVP using web-based data analytic tools to improve efficiency and accuracy in each project. This paper used a web-based mathematical computation, Jupyter Notebook, to determine the significant independent variable for routine adjustment of the adjusted baseline. The program tool was validated using V-model software verification and validation methodology based on IEEE 1012 standards. The study can be concluded that the exhaustive calculation to determine the energy saving based on IPMVP standards can be significantly reduced with precision values at 50%, 80%, 90%, and 95% confidence levels using the proposed program which is equipped with insightful graphs and a user-friendly interface.

Impact of Consumer Buying Behavior on Satisfaction Towards Dairy Products: An Empirical Study

Purpose: This study aims to investigate consumer buying behavior towards government-owned dairy products in India and assess their level of satisfaction.
 
 Design/Methodology/Approach: The researcher employed a simple random sampling technique to select 576 consumers from across India. Various data analysis tools were applied to the collected data to draw conclusions.
 
 Findings: The research found a significant association between demographic variables and customer satisfaction. Factors like "Product Quality," "Product Value," and "Awareness" were identified as key influencers of customer satisfaction. Additionally, the study revealed a direct negative impact of buying behavior on customer satisfaction, with purchase intention and customer service acting as mediators with positive effects.
 
 Research, Practical & Social implications: This study sheds light on the importance of product quality and value in influencing customer buying behavior. It has practical implications for dairy product marketers, emphasizing the need to prioritize quality and value during processing. Socially, it highlights the importance of consumer awareness in ensuring the quality and safety of dairy products.
 
 Originality/Value: This research contributes to understanding consumer behavior in the context of government-supplied dairy products in India. It emphasizes the originality and value of considering factors like awareness and customer service in assessing customer satisfaction, offering insights for improving marketing strategies in the dairy industry.

Utilizing Big Data Analytics and Business Intelligence for Improved Decision-Making at Leading Fortune Company

The present study evaluates Walmart’s existing big data analytics with business intelligence techniques, accentuating their strengths and weaknesses, and suggests improvements for implementation and maintenance through the literature review of the scholarly journals addressing similar topics. Big data analytics is receiving loads of attention globally in the business environment within every sector of the economy. Incorporating the job plan as an additional input component in their models would be beneficial for Walmart to improve the precision and appropriateness of their data analysis and decision-making procedures. Walmart is a company that heavily invests in utilizing big data to improve its operations; this includes optimizing in-store experiences and predicting product trends. Scholarly articles emphasize the importance of advanced data analytics tools like MapReduce and Apache Spark for effective big data strategies. Social media content influences engagement and sentiment. Social network data aids sales forecasting but presents challenges. Big data analytics with business intelligence enhances performance and decision-making. Walmart's success in big data analytics relies on a data-driven culture but faces security challenges. Many Fortune 1000 companies adopt innovative solutions to improve performance and customer experiences but require significant resources. Embracing big data analytics with business intelligence remains a compelling investment for sustaining a competitive edge. Walmart's success in big data analytics is due to a data-driven culture and advanced infrastructure, including the Data Café.

Pump availability prediction using response surface method in nuclear plant

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.

PENGARUH PERPUTARAN PERSEDIAAN TERHADAP LIKUIDITAS DAN DAMPAKNYA TERHADAP PROFITABILITAS

The purpose of this study was to determine and analyze the effect of inventory turnover on liquidity as measured by quick ratios and its impact on profitability through the operating profit margin at PT. Semen Indonesia (Persero) Tbk. The research method used is quantitative with a causal method and a case study approach. The data used is secondary data because the data obtained is from the annual and quarterly financial reports of PT. Semen Indonesia (Persero) Tbk., period 2012-2022. The data analysis tool used is path analysis. Based on the research result, it is known that inventory turnover has a positive and insignificant direct effect on liquidity (quick ratios). Inventory turnover has a positive and insignificant direct effect on profitability (operating profit margin). Inventory turnover has no significant effect on profitability through liquidity

“I guess it's quite trendy”: A qualitative insight into young meat-eaters’ sustainable food consumption habits and perceptions towards current and future protein alternatives

As the market for sustainable food continues to expand, there is a need to understand how consumers’ consumption habits and perceptions are changing. Targeting the younger populations is of interest as they arguably will shape the future of food. Therefore, the present study aimed to provide in-depth consumer insights on a range of topics from current consumption habits (i.e., meat reduction, plant-based meat/seafood (PBM/S)), towards future protein alternatives (i.e., cell-based meat/seafood (CBM/S), precision fermented dairy (PFD)). Online focus groups were conducted in the UK with meat-eaters (n = 38) aged 18–34. Codebook thematic analysis was applied using the Framework Matrix as a tool for data analysis. Key themes were presented using the COM-B model (Capability, Opportunity, Motivation), which identified areas of behavioural change. Results found a trend towards meat reduction, partially initiated by moving away from home and limited food budgets. Overall, participants acknowledged the environmental impact of food, but a notable knowledge gap was apparent when quantifying the effect, especially for dairy and seafood. Compared to PBM, few participants had tried PBS products, partially due to lower availability and familiarity. Enablers for PBM/S included convenience, positive sensory experiences and the influence of others, whilst barriers related to negative health connotations and over-processing. For CBM/S and PFD, animal welfare, curiosity and optimised nutrition acted as enablers, whilst barriers related to wider consumer acceptance, affordability and unnaturalness. In general, participants felt changing food consumption habits can have an impact on climate change and were optimistic about novel technologies supporting future protein transitions. Increasing public understanding around the environmental impact of food, especially seafood and dairy, and prioritising the affordability of sustainable food are suggested as intervention strategies to encourage sustainable food consumption.

Aruna Object Storage

The exponential growth of scientific data has led to an increasing demand for effective data management and storage solutions. Academic computing infrastructures are often fragmented, which can make it challenging for researchers to leverage cloud-native principles and modern data analysis tools. To address this challenge, a new distributed storage platform called Aruna Object Storage (AOS) was developed. AOS is a cloud-native, scalable, and domain-agnostic object storage system that provides an S3-compatible interface for a variety of data analysis tools like Apache Spark, TensorFlow, and Pandas. The system uses an underlying distributed NewSQL database to manage detailed information about its resources and can be deployed across multiple data centers for geo-redundancy. AOS is designed to support modern DataOps practices, including the adoption of FAIR principles. Resources in AOS are organized into Objects, Datasets, Collections and Projects, which represent relations of data objects. Additionally, these can be further annotated with key-value pairs called Labels and Hooks to provide additional information about the data. The system's event-driven architecture makes it easy to automate actions and enforce data validation checks, significantly improving accessibility and reproducibility of scientific results. AOS is open source and freely available via https://aruna-storage.org.

Design of the Multi-purpose Marketing Data Analysis Program: Using actual online sales data

Marketing is a strategy for the sale of goods. Most shopping malls and vendors value this, and it is also a measure of the company's success. To this end, strategies are established through marketing departments or specialized companies, and related data is collected directly. Data analysis uses some tools, but in the end, data must be collected directly, and most of the tools that collect automatically only use SNS networks. In this paper, we present a design method for programs that can be fundamentally utilized without customizing or providing the functions of existing data analysis tools. In the program, HTML structures for commonly used shopping malls, not SNS, are predefined and prepared for immediate use of crawling techniques, and some textual data are generated as Excel files. This can be applied in real time, and some artificial intelligence and emotional analysis are applied to the image to help make an objective evaluation.

Pengaruh Job Burnout terhadap Turnover Intention pada Generasi Milenial Indonesia

Employee intention to leave is an individual's attitude to leave the organization because of fatigue at work, so it makes the intention to leave the company. This study has the goal of knowing the conditions and effects of work fatigue on employee intention to leave in the millennial generation in Indonesia so that it can enrich understanding and thinking about the effect of work fatigue on employee intention to leave, as well as being a company reference in determining strategies to minimize the level of employee intention to leave. The method used is a causal descriptive technique with a quantitative approach. The population of this study is the Indonesian millennial generation. The research sample is three hundred and sixty respondents—data collection using online questionnaires and Structural Equation Model data analysis tools. This study concludes that work fatigue positively and significantly affects employee intention to leave. Thus, it is suggested that the company can seriously handle work fatigue so that there is no intention to leave the company.

Using the RPM Model to Explore the Impact of Organic Agritourism Destination Fascination on Loyalty—The Mediating Roles of Place Attachment and Pro-Environmental Behavior

Maintaining the sustainable development of fascinating resources in Taiwan’s Hualien and Taitung (Huatung) regions is the basis of organic agritourism. Loyalty not only represents tourists’ commitment to return and recommend but also serves as a goal for destination development. The purpose of this study is to propose and examine the effect of destination fascination on loyalty through a conceptualized Reasonable Person Model (RPM), mediated by place attachment and pro-environmental behaviors. SPSS21 and the linear structure relationship model (LISREL) were used as data analysis tools in this study. A total of 500 valid online questionnaires were collected through snowballing and convenience sampling. The data analysis results revealed that destination fascination has a positive and significant impact on loyalty through place attachment and pro-environmental behavior. Another finding is that place dependence, place identity, and pro-environmental behavior all have mediating effects, with place identity being the main mediating variable. The contributions of this study are mainly reflected in examining the application of RPM in fascination and loyalty, as well as providing practical suggestions for destination fascination and place identity.

(Re-)use and (re-)analysis of publicly available metabolomics data.

Metabolomics, the systematic measurement of small molecules (<1000Da) in a given biological sample, is a fast-growing field with many different applications. In contrast to transcriptomics and proteomics, sharing of data is not as widespread in metabolomics, though more scientists are sharing their data nowadays. However, to improve data analysis tools and develop new data analytical approaches and to improve metabolite annotation and identification, sharing of reference data is crucial. Here, different possibilities to share (metabolomics) data are reviewed and some recent approaches and applications regarding the (re-)use and (re-)analysis are highlighted.

Exploiting organizations' innovation performance via big data analytics: an absorptive knowledge perspective

PurposeThe advantages of applying big data analytics for organizations to boost innovation performance are enormous. By collecting and analysing substantial amounts of data, firms can discern what works for their customer needs and update existing products while innovating new ones. Notwithstanding the evidence about the effects of big data analytics, the link between big data analytics and innovation performance is still underestimated. Especially in today's fast-changing and complicated environments, companies cannot simply take big data analytics as one innovative technical tool without fully understanding how to deploy it effectively.Design/methodology/approachThis study tries to investigate this relationship by building on the knowledge absorptive capacity perspective. The authors conceptualized effective use of big data analytics tools as one general absorptive capacity rather than a simple technical element or skill. Specifically, effectively utilize big data analytics tools can provide values and insights for new product innovation performance in a turbulent environment. Using online survey data from 108 managers, the authors assessed their hypotheses by applying the structural equation modelling method.FindingsThe authors found that big data analytics capacity, which can be conceptualized as one absorptive capacity, can positively influence product innovation performance. The authors also found that environmental turbulence has strong moderation effects on these two main relationships.Originality/valueThese results establish big data analytics can be regarded as one absorptive capacity, which can positively boost an organization's innovation performance.

A Reference Architecture for Supporting Multidimensional Big Data Analytics over Big Web Knowledge Bases: Definitions, Implementation, Case Studies

This paper focuses attention on the problem of supporting multidimensional big data analytics tools over Big Web Knowledge Bases. These big data repositories are generated from web systems built on top of big datasets, as in several real-life examples such as web interfaces to social networks, e-government systems, smart city tools (e.g., for weather forecasting), and so forth. The problem of extracting actionable knowledge insights from big web knowledge bases is a relevant challenge now. In line with this emerging research field, the paper proposes models, issues and research trends in the context of multidimensional big data analytics over big web knowledge bases, along with a reference architecture that impersonates this paradigm.

IMPACT OF MOBILE BANKING ON CUSTOMERS’ SATISFACTION IN DEPOSIT MONEY BANK IN NIGERIA

The study examined the impact of mobile banking on customer satisfaction in United Bank for Africa. Bank customers in Nigeria have been found to be dissatisfied with the quality, of services provided by banks. This calls for the need to actually investigate how customers behave or get satisfied with regards to the services of commercial banks in Nigeria. No doubt that information from current technologies in banking operations has improved efficiency and effectiveness of their operations so that more transactions can be processed faster and most conveniently. The main objective of this study is to examine the impact of mobile banking on customer satisfaction in United Bank for Africa. Data was source basically from the primary source. Primary data are those data gotten from the field of study. Questionnaire was used as instrument of data collection, regression was used as a tool for data analysis. The findings of the study revealed that Automated Teller Machine (ATM), internet banking, telephone banking and mobile banking has significant impact on service delivery to customers. The study recommended that banks should provide more functional infrastructures to strengthen their e-business transaction. Standby electric power generating sets and inverters be provided to power the banking halls, information technology equipment and ATMs respectively whenever there is public power failure to ensure continued smooth operations.

Comparative Analysis of Fruit Disease Identification Methods: A Comprehensive Study

The need for accurate and efficient technologies for recognising and controlling fruit diseases has increased due to the rising global demand for high-quality agricultural products. This study focuses on the advantages, disadvantages, and potential practical applications of a range of methods for identifying fecundities. Thanks to developments like improved imaging, machine learning, and data analysis tools, old methods of disease diagnosis have altered as technology has developed. The study compares older methods like visual observation, manual symptom correlation, spectroscopy, and chemical procedures with more contemporary methods like computer vision, autonomous learning algorithms, and sensor-based technologies. Precision, efficiency, cost, scalability, and ease of use are used to describe each method's effectiveness. The article reviews the research examples and practical applications of fruit endocrine disease detection in different cultivars and areas to provide a thorough comparison. This comparison focuses on the variations in disease prevalence and the ways that alternative treatments can be customised to certain situations.It is for this reason that this study offers useful information on how the methods for detecting fruit rot have evolved through time. It emphasises the significance of utilising technological advances to enhance the accuracy, effectiveness, and long-term sustainability of the management of agricultural diseases. Based on the unique requirements of their various agricultural systems, this analysis can assist researchers, practitioners, and policymakers in selecting the most effective methods for identifying fruit diseases.