Decision making system with Big Data Analytics in modern days of business

2020 ◽  
Author(s):  
praveen kumar
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Free Solution ◽  
Decision Making System ◽  
Critical Process

Decision support system (DSS) in today’s world in era of Big Data Analytics (BDA) has shifted many fold from manual interpretation (Management centric) to more distributed hierarchy. More logical and accurate with BDA Ecosystem from judiciary to insurance. This paper will talk about sectors that has successfully adapted it. DSS with Predictive, prescriptive and descriptive analytics with data like visual, audio, syntactical, raw that too in auto mode as to give more accurate and interruption free solution to critical process. Hence aim is understand BDA to DSS implementation with modern and open source technologies like Hadoop, AWS, Apache suite etc.

scholarly journals Big Data Analytics with OENN Based Clinical Decision Support System

2022 ◽  
Vol 31 (2) ◽  
pp. 1241-1256
Author(s):  
Thejovathi Murari ◽  
L. Prathiba ◽  
Kranthi Kumar Singamaneni ◽  
D. Venu ◽  
Vinay Kumar Nassa ◽  
...  
Keyword(s):  
Big Data ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Clinical Decision

scholarly journals TOPSIS Method Application for Decision Support System in Internal Control for Selecting Best Employees

2018 ◽  
Author(s):  
Andysah Putera Utama Siahaan ◽  
Robbi Rahim ◽  
Tri Listyorini
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Internal Control ◽  
Computational Method ◽  
Top Management ◽  
Job Responsibilities ◽  
Decision Making System ◽  
Selection Of

The selection of the best employees is one of the process of evaluating how well the performance of the employees is adjusted to the standards set by the company and usually done by top management such as General Manager or Director. In general, the selection of the best employees is still perform manually with many criteria and alternatives, and this usually make it difficult top managerial making decisions as well as the selection of the best employees periodically into a long and complicated process. Therefore, it is necessary to build a decision support system that can help facilitate the decision maker in determining the best choice based on standard criteria, faster, and more objective. In this research, the computational method of decision-making system used is Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The criteria used in the selection of the best employees are: job responsibilities, work discipline, work quality, and behaviour. The final result of the global priority value of the best employee candidates is used as the best employee selection decision making tool by top management.

scholarly journals Implementasi Fuzzy ELECTRE Untuk Penilaian Kerusakan Akibat Bencana Alam

2017 ◽  
Vol 7 (2) ◽  
pp. 81
Author(s):  
Agusta Praba Ristadi Pinem
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Natural Disasters ◽  
Support System ◽  
Integrated System ◽  
System Combination ◽  
Decision Making System ◽  
Electre Method ◽  
Ranking Information

Indonesia is prone to geology natural disasters, such as the earthquake and tsunami. The research aims to develop decision support system that can be used for assessment of damage caused by natural disaster after disaster. The method used in this system is method of fuzzy   ELECTRE and ECLAC. Fuzzy ELECTRE method used to determine the priority of the affected area, while ECLAC method used to calculate the natural disasters damage. Fuzzy method used in the process of assessing the weight and classification process. Variables used in this research is the sector that were damaged. Integration of fuzzy and ELECTRE methods applied in the prioritization of areas affected by natural disasters. The result of this research is classification of disaster area with rank based on weighting. Output visualized decision-making system in the form of dashboards in the form of mapping the area affected by the disaster. The variable rate of interest was made as a decision maker in the form of mapping that indicates the priority areas most severely affected by natural disasters. The process of calculation, analysis and ranking built in one integrated system. Combination mapping and decision support system produce information to support decision making. Ranking information similar with priority in BNPB documents with 0.96 coefficient Rank Spearman correlation (ρ).

Decision Support Systems

2020 ◽  
Author(s):  
Sean B. Eom
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Decision Support ◽  
Decision Support Systems ◽  
Business Intelligence ◽  
Data Analytics ◽  
Support Systems ◽  
Academic Discipline ◽  
Big Data Analytics ◽  
Academic Field

A decision support system is an interactive human–computer decision-making system that supports decision makers rather than replaces them, utilizing data and models. It solves unstructured and semi-structured problems with a focus on effectiveness rather than efficiency in decision processes. In the early 1970s, scholars in this field began to recognize the important roles that decision support systems (DSS) play in supporting managers in their semi-structured or unstructured decision-making activities. Over the past five decades, DSS has made progress toward becoming a solid academic field. Nevertheless, since the mid-1990s, the inability of DSS to fully satisfy a wide range of information needs of practitioners provided an impetus for a new breed of DSS, business intelligence systems (BIS). The academic discipline of DSS has undergone numerous changes in technological environments including the adoption of data warehouses. Until the late 1990s, most textbooks referred to “decision support systems.” Nowadays, many of them have replaced “decision support systems” with “business intelligence.” While DSS/BIS began in academia and were quickly adopted in business, in recent years these tools have moved into government and the academic field of public administration. In addition, modern political campaigns, especially at the national level, are based on data analytics and the use of big data analytics. The first section of this article reviews the development of DSS as an academic discipline. The second section discusses BIS and their components (the data warehousing environment and the analytical environment). The final section introduces two emerging topics in DSS/BIS: big data analytics and cloud computing analytics. Before the era of big data, most data collected by business organizations could easily be managed by traditional relational database management systems with a serial processing system. Social networks, e-business networks, Internet of Things (IoT), and many other wireless sensor networks are generating huge volumes of data every day. The challenge of big data has demanded a new business intelligence infrastructure with new tools (Hadoop cluster, the data warehousing environment, and the business analytical environment).

Research on decision support system of sports assistant teaching and training based on association rules and support vector machine

2021 ◽  
pp. 1-12
Author(s):  
Tong-Zhigang
Keyword(s):  
Decision Making ◽  
Support Vector Machine ◽  
Decision Support ◽  
Decision Support System ◽  
Association Rules ◽  
Support System ◽  
Support Vector ◽  
Learning And Teaching ◽  
Decision Making System ◽  
And Training

In the decision-making system of sports assistant teaching and training, the performance of such system is not robust to different situations with a low accuracy. To solve the problems in the decision-making system, we proposed a decision-making method combining association rules and support vector machine (SVM) in this paper. First of all, we give a computer-aided decision support system for sports assistant learning and teaching training, which is fully elaborated from three aspects: virtual reality (VR) technology, VR based sports assistant learning and teaching and situational cognition, and VR based sports assistant learning and teaching mode. After that, the paper gives the feature extraction of sports auxiliary teaching training through association rules and the decision-making of the extracted association rules by SVM. We have done two different experiments for both association rules mining and SVM on both experiment group and control group of databases. Experimental results have shown that the training characteristics of sports auxiliary teaching very well. In the decision support of association rules, compared with the existing BP neural network, linear discriminant analysis and naive Bayes and other methods, the SVM method has better effect of action recognition in decision support system of sports assistant teaching and training. The robustness is the best for the application of SVM. We provide a new perspective for the decision support of sports auxiliary teaching training by using association rules and SVM. Through the method of this paper, we can obtain better decision-making effect and more robust process of sports auxiliary teaching and training.

scholarly journals DECISION SUPPORT SYSTEMS INCREASE RANK USING AHP WEB – BASED ON PT TAMBANG BUKIT ASAM (PERSERO) TBK

2021 ◽  
Vol 9 (1) ◽  
pp. 1-6
Author(s):  
Wahyuni Fithratul Zalmi
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Assessment Process ◽  
Decision Making Process ◽  
Web Based ◽  
Decision Making System ◽  
Application Decision ◽  
Hierarchy Process

Decision support system is a system adopted from human knowledge, which can help people in the decision making process. In research conducted at PT Coal Bukit Asam (Persero) authors see most of the systems currently used in the search process of promotion of employees still in manual form, causing the length of the assessment process associated with a promotion for every employee of the company in accordance with any the criteria that have been specified company. Various weighting of criteria and sub criteria established companies are always considered manager to each process of inputting assessment. Therefore, the authors make a decision support system application manager that can facilitate the process of inputting weight rating decision promotion of employees in accordance with the criteria and sub-criteria that have been specified company. Application decision support system of promotion is designed using Analytical Hierarchy Process that the process to obtain the priority weighting is done by comparing each criteria and sub-criteria. Decision to be achieved can help managers to determine who the employees are entitled to a promotion in accordance with keptusan obtained from the application of this decision-making system.

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