scholarly journals Literature Study on the Use of Big Data and Artificial Intelligence in Policy Making in Indonesia

2021 ◽  
Vol 12 (2) ◽  
pp. 139-153
Author(s):  
Eko Eddya Supriyanto ◽  
◽  
Hardi Warsono ◽  
Augusin Rina Herawati ◽  
◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Big Data ◽  
Policy Making ◽  
Public Services ◽  
Literature Study ◽  
Business World ◽  
Study Approach ◽  
Public Leaders ◽  
Fast Decision

The use of big data and artificial intelligence in decision-making in Indonesia is still rarely implemented. But in the business world, big data and artificial intelligence are very commonplace to boost targets. This study discusses the use of big data and artificial intelligence in policy Making in Indonesia. The method used in this paper is qualitative research with a literature study approach. The result of this research is that the dynamics in the implementation of public services require appropriate and fast decision making, considering that this is a community demand. Therefore, public leaders need to disrupt themselves in public services so that these services can be served quickly. In conclusion, big data and artificial intelligence can help public leaders make decisions to deliver the best policies. This research implies that it can be used as a reference for policymakers that big data and artificial intelligence can be used in decision-making to warn Policymaking.

Who’s making the decisions? How managers can harness artificial intelligence and remain in charge

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pooya Tabesh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Big Data ◽  
Digital Transformation ◽  
Decision Processes ◽  
Decision Makers ◽  
Content Type ◽  
Human Decision ◽  
Practical Implications

Purpose While it is evident that the introduction of machine learning and the availability of big data have revolutionized various organizational operations and processes, existing academic and practitioner research within decision process literature has mostly ignored the nuances of these influences on human decision-making. Building on existing research in this area, this paper aims to define these concepts from a decision-making perspective and elaborates on the influences of these emerging technologies on human analytical and intuitive decision-making processes. Design/methodology/approach The authors first provide a holistic understanding of important drivers of digital transformation. The authors then conceptualize the impact that analytics tools built on artificial intelligence (AI) and big data have on intuitive and analytical human decision processes in organizations. Findings The authors discuss similarities and differences between machine learning and two human decision processes, namely, analysis and intuition. While it is difficult to jump to any conclusions about the future of machine learning, human decision-makers seem to continue to monopolize the majority of intuitive decision tasks, which will help them keep the upper hand (vis-à-vis machines), at least in the near future. Research limitations/implications The work contributes to research on rational (analytical) and intuitive processes of decision-making at the individual, group and organization levels by theorizing about the way these processes are influenced by advanced AI algorithms such as machine learning. Practical implications Decisions are building blocks of organizational success. Therefore, a better understanding of the way human decision processes can be impacted by advanced technologies will prepare managers to better use these technologies and make better decisions. By clarifying the boundaries/overlaps among concepts such as AI, machine learning and big data, the authors contribute to their successful adoption by business practitioners. Social implications The work suggests that human decision-makers will not be replaced by machines if they continue to invest in what they do best: critical thinking, intuitive analysis and creative problem-solving. Originality/value The work elaborates on important drivers of digital transformation from a decision-making perspective and discusses their practical implications for managers.

Big Data, Semantics, and Policy-Making

2019 ◽  
pp. 154-178
Author(s):  
Lamyaa El Bassiti
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Policy Making ◽  
Policy Design ◽  
Semantic Technologies ◽  
Design And Implementation ◽  
Policy Choices ◽  
Data Semantics ◽  
Policy Argument

At the heart of all policy design and implementation, there is a need to understand how well decisions are made. It is evidently known that the quality of decision making depends significantly on the quality of the analyses and advice provided to the associated actors. Over decades, organizations were highly diligent in gathering and processing vast amounts of data, but they have given less emphasis on how these data can be used in policy argument. With the arrival of big data, attention has been focused on whether it could be used to inform policy-making. This chapter aims to bridge this gap, to understand variations in how big data could yield usable evidence, and how policymakers can make better use of those evidence in policy choices. An integrated and holistic look at how solving complex problems could be conducted on the basis of semantic technologies and big data is presented in this chapter.

scholarly journals Deep Learning Algorithms and Multicriteria Decision-Making Used in Big Data: A Systematic Literature Review

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-18 ◽  
Author(s):  
Mei Yang ◽  
Shah Nazir ◽  
Qingshan Xu ◽  
Shaukat Ali
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Multicriteria Decision Making ◽  
Literature Study ◽  
Multicriteria Decision ◽  
Challenging Tasks ◽  
Critical Issues ◽  
Different Shapes

The data are ever increasing with the increase in population, communication of different devices in networks, Internet of Things, sensors, actuators, and so on. This increase goes into different shapes such as volume, velocity, variety, veracity, and value extracting meaningful information and insights, all are challenging tasks and burning issues. Decision-making based on multicriteria is one of the most critical issues solving ways to select the most suitable decision among a number of alternatives. Deep learning algorithms and multicriteria-based decision-making have effective applications in big data. Derivations are made based on the use of deep algorithms and multicriteria. Due to its effectiveness and potentiality, it is exploited in several domains such as computer science and information technology, agriculture, and business sector. The aim of the proposed study is to present a systematic literature study in order to show the applications of deep learning algorithms and multicriteria decision approaches for the problems of big data. The research finds novel means to make the decision support system for the problems of big data using multiple criteria in integration with machine learning and artificial intelligence approaches.

Policy Making and Rationality in Sweden: The Case of Transport

1979 ◽  
Vol 9 (3) ◽  
pp. 341-353 ◽  
Author(s):  
J. J. Richardson
Keyword(s):  
Decision Making ◽  
Political Culture ◽  
Policy Making ◽  
Political System ◽  
Political Systems ◽  
Study Approach ◽  
Realistic View ◽  
Individual Decisions ◽  
The Comparative Study

It is not uncommon for the case-study approach to the study of policy making to be criticized for failing to produce broad generalizations or concepts applicable to systems as a whole. For example, in discussing policy making and political culture in Sweden, Anton argues that case-studies of decision making do not offer an adequate or realistic view of the process. In doing so he suggests that we should shift our focus of concern ‘from the single decision (whatever it is), to the structure of relationships between participants and the norms which serve to maintain or change those relationships through time. The focus shifts, in other words, from decisions to systems of decision-making’. Whilst not disputing the need for studying the general properties of decision making in a given political system (or indeed the need for the comparative study of policy making in different systems), this article will suggest the value of studying individual policy areas – in this case, transport – as opposed to both individual decisions and entire political systems, as a means of testing broader propositions such as those formulated by Anton.

scholarly journals How can we discover the most valuable types of big data and artificial intelligence-based solutions? A methodology for the efficient development of the underlying analytics that improve care

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Lytske Bakker ◽  
Jos Aarts ◽  
Carin Uyl-de Groot ◽  
Ken Redekop
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Big Data ◽  
Intensive Care ◽  
Best Practice ◽  
Investment Decisions ◽  
Cost Savings ◽  
Economic Evaluations ◽  
Successful Implementation ◽  
Practice Methods

Abstract Background Much has been invested in big data and artificial intelligence-based solutions for healthcare. However, few applications have been implemented in clinical practice. Early economic evaluations can help to improve decision-making by developers of analytics underlying these solutions aiming to increase the likelihood of successful implementation, but recommendations about their use are lacking. The aim of this study was to develop and apply a framework that positions best practice methods for economic evaluations alongside development of analytics, thereby enabling developers to identify barriers to success and to select analytics worth further investments. Methods The framework was developed using literature, recommendations for economic evaluations and by applying the framework to use cases (chronic lymphocytic leukaemia (CLL), intensive care, diabetes). First, the feasibility of developing clinically relevant analytics was assessed and critical barriers to successful development and implementation identified. Economic evaluations were then used to determine critical thresholds and guide investment decisions. Results When using the framework to assist decision-making of developers of analytics, continuing development was not always feasible or worthwhile. Developing analytics for progressive CLL and diabetes was clinically relevant but not feasible with the data available. Alternatively, developing analytics for newly diagnosed CLL patients was feasible but continuing development was not considered worthwhile because the high drug costs made it economically unattractive for potential users. Alternatively, in the intensive care unit, analytics reduced mortality and per-patient costs when used to identify infections (− 0.5%, − €886) and to improve patient-ventilator interaction (− 3%, − €264). Both analytics have the potential to save money but the potential benefits of analytics that identify infections strongly depend on infection rate; a higher rate implies greater cost-savings. Conclusions We present a framework that stimulates efficiency of development of analytics for big data and artificial intelligence-based solutions by selecting those applications of analytics for which development is feasible and worthwhile. For these applications, results from early economic evaluations can be used to guide investment decisions and identify critical requirements.

scholarly journals Model E-Readiness Untuk Pengukuran Kesiapan Pengelolaan Aduan E-Lapor DIY

2021 ◽  
Vol 7 (2) ◽  
pp. 86
Author(s):  
Dwi Lestari ◽  
Wing Wahyu Winarno ◽  
Mei P Kurniawan
Keyword(s):  
Public Services ◽  
Regional Government ◽  
Literature Study ◽  
Important Thing ◽  
Study Approach ◽  
Country Level ◽  
Ict Infrastructure ◽  
The Government ◽  
The Right ◽  
Selection Of

Pemanfaatan TIK secara efektif dan efisien telah digunakan untuk meningkatkan pelayanan publik yang diselenggarakan oleh Pemerintah. Pemerintah Daerah DIY telah mengembangkan aplikasi aduan E-Lapor sejak 2018, akan tetapi pengelolaanya aduan yang masuk belum terespon tepat waktu sesuai dengan SOP E-Lapor DIY dimana aduan yang masuk harus direspon paling lambat 5 hari kerja. Sehingga untuk mengetahui hambatan dan kendala dalam pengelolaan E-Lapor DIY diperlukan pengukuran kesiapan faktor-faktor dalam pengelolaan E-Lapor DIY. Suksesnya pengelolaan E-lapor DIY bukan hanya dipengaruhi oleh faktor infrastruktur TIK saja, akan tetapi faktor yang turut mempengaruhi tingkat kesiapan. Telah dikembangkan pada tingkat negara tertentu beberapa model e-readiness yang mengidentifikasi dari faktor-faktor perspektif makro. Sebelum melakukan pengukuran e-readiness terdapat hal yang krusial yang perlu dilakukan yaitu dengan pemilihan model e-readiness yang tepat. Penelitian ini menggunakan pendekatan studi literatur dengan membandingkan beberapa model e-readiness yang telah popular dan sudah sering diadopsi dalam penelitian di Indonesi. Penelitian ini akan menghasilkan sebuah rekomendasi model e-readiness Mutula Brakel yang paling sesuai untuk pengukuran kesiapan dalam pengelolaan aduan melalui aplikasi E-lapor DIY.Kata Kunci— e-lapor, e-readiness, tik, mutula dan brakel Effective and efficient use of ICT has been used to improve public services provided by the Government. The Regional Government of DIY has developed the E-Lapor complaint application since 2018, but the management of complaints that come in yet responded on time in accordance with the E-Lapor DIY procedure where the incoming complaint must be responded to no later than 5 working days. Efforts to determine the obstacles in managing E-Lapor DIY are needed to measure the readiness of the factors in managing E-Lapor DIY. The success of E-Lapor DIY management is not only influenced by ICT infrastructure factors, but also factors that influence the level of readiness. There are several e-readiness models that have been developed by identifying factors from a macro perspective at a particular country level. The selection of the right e-readiness model is an important thing that needs to be done, before measuring e-readiness. This research will compare several popular e-readiness models and are often adopted in research in Indonesia with a literature study approach. This study produces recommendations for the Mutula Brakel e-readiness model that is suitable for measuring readiness in complaint management through the E-Lapor DIY application. Keywords— e-lapor, e-readiness, ICT, mutula dan brakel

Analytics in Public Policy Related to Service Sector

2017 ◽  
pp. 34-53
Author(s):  
Maryam Ebrahimi
Keyword(s):  
Public Policy ◽  
Decision Making ◽  
Big Data ◽  
Competitive Advantage ◽  
Rapid Growth ◽  
Financial Services ◽  
Policy Making ◽  
Service Sector ◽  
Fraud Detection ◽  
Tools And Techniques

Big Data is transforming industries such as healthcare, financial services and banking, insurance, pharmacy, and telecommunication. Big Data concerns datasets that are not only big, but also high in variety and velocity, which makes them difficult to manage applying traditional tools and techniques. Big Data causes multitude benefits and advantages for industries such as marketing and selling, fraud detection, competitive advantage, risk reduction, and finally decision making and policy making. Due to the rapid growth of such data, methodologies and conceptual architectures need to be studied and provided in order to handle and extract value and knowledge from these data. The purpose of this chapter is studying Big Data benefits, characteristics, methodologies, and conceptual architectures in five different industries. Finally, according to the studies, a comprehensive methodology and architecture are proposed which might be applicable in service sector and one of the useful outcomes can be public policies.

Intelligent Big Data Analytics

2019 ◽  
pp. 1-19 ◽  
Author(s):  
Zhaohao Sun
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
Business Decision ◽  
Technology System ◽  
System Service ◽  
Managerial Perspective

Intelligent big data analytics is an emerging paradigm in the age of big data, analytics, and artificial intelligence (AI). This chapter explores intelligent big data analytics from a managerial perspective. More specifically, it first looks at the age of trinity and argues that intelligent big data analytics is at the center of the age of trinity. This chapter then proposes a managerial framework of intelligent big data analytics, which consists of intelligent big data analytics as a science, technology, system, service, and management for improving business decision making. Then it examines intelligent big data analytics for management taking into account four managerial functions: planning, organizing, leading, and controlling. The proposed approach in this chapter might facilitate the research and development of intelligent big data analytics, big data analytics, business intelligence, artificial intelligence, and data science.

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