scholarly journals DATA MINING (KLASTERISASI) PERBANDINGAN MAHASISWA YANG MENDAFTAR TERHADAP MAHASISWA YANG DITERIMA

2021 ◽  
Vol 3 (1) ◽  
pp. 59-70
Author(s):  
Walhidayat Walhidayat ◽  
Mariza Devega ◽  
Susi Handayani
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Computer Science ◽  
Research Team ◽  
Data Sources ◽  
Basic Information ◽  
Special Study ◽  
Policy Makers ◽  
A Company ◽  
Support Decision Making

The problem with data owned by a company or agency is data as a benchmark for basic information. However, these data are generally in a developing country or companies that have not utilized the sophisticated structure of information systems in the current era still see the data as a part of a process, which results in the data being used separately as a container of information to support decision making. . There is a need for a special study conducted by the research team to look at this problem subjectively, namely by sorting out the identification of data sources into useful information for leaders or officials. There are several methods that can be used to mine data (data mining), including: Classification, Clustering, Association and various appropriate methods can be used. The results of data mining can be information that can be easily understood by policy makers in an institution, especially leaders at the Faculty of Computer Science.

scholarly journals Failure Analysis in University and Computer Science Contexts With Data Mining

2020 ◽  
Author(s):  
Daniela De Souza Gomes ◽  
Marcos Henrique Fonseca Ribeiro ◽  
Giovanni Ventorim Comarela ◽  
Gabriel Philippe Pereira
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Failure Analysis ◽  
Computer Science ◽  
Educational Administration ◽  
Intelligent Systems ◽  
Data Set ◽  
Data Mining Techniques ◽  
Study Case ◽  
Support Students

High failure rates are a worrying and relevant problem in Brazilian universities. From a data set of student transcripts, we performed a study case for both general and Computer Science contexts, in which Data Mining Techniques were used to find patterns concerning failures. The knowledge acquired can be used for better educational administration and also build intelligent systems to support students’ decision making.

A Conceptual Model for Describing the Integration of Decision Aspect into Big Data

2022 ◽  
pp. 294-318
Author(s):  
Fatma Chiheb ◽  
Fatima Boumahdi ◽  
Hafida Bouarfa
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Competitive Advantage ◽  
Conceptual Model ◽  
Big Data Analytics ◽  
Decision Makers ◽  
Modern Business ◽  
Definition Of ◽  
A Company ◽  
Support Decision Making

Big Data is an important topic for discussion and research. It has gained this importance due to the meaningful value that could be extracted from these data. The application of Big Data in the modern business allows enterprises to take faster and smarter decisions, achieving a real competitive advantage. However, a lot of Big Data projects provide disappointing results that don't address the decision-makers' needs due to many reasons. The main reason for this failure can be summarized in neglecting the study of the decision-making aspect of these projects. In light of this challenge, this study proposes the integration of decision aspect into Big Data as a solution. Therefore, this article presents three main contributions: 1) Clarify the definition of Big Data; 2) Presents BD-Da model, a conceptual model describes the levels that should be considered to develop a Big Data project aiming to solve a problem that calls a decision; 3) Describes a particular, logical, requirements-like approach that explains how a company develops a Big Data analytics project to support decision-making.

A Conceptual Model for Describing the Integration of Decision Aspect into Big Data

2019 ◽  
Vol 10 (4) ◽  
pp. 1-23
Author(s):  
Fatma Chiheb ◽  
Fatima Boumahdi ◽  
Hafida Bouarfa
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Conceptual Model ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Decision Makers ◽  
Modern Business ◽  
Definition Of ◽  
A Company ◽  
Support Decision Making

Big Data is an important topic for discussion and research. It has gained this importance due to the meaningful value that could be extracted from these data. The application of Big Data in the modern business allows enterprises to take faster and smarter decisions, achieving a real competitive advantage. However, a lot of Big Data projects provide disappointing results that don't address the decision-makers' needs due to many reasons. The main reason for this failure can be summarized in neglecting the study of the decision-making aspect of these projects. In light of this challenge, this study proposes the integration of decision aspect into Big Data as a solution. Therefore, this article presents three main contributions: 1) Clarify the definition of Big Data; 2) Presents BD-Da model, a conceptual model describes the levels that should be considered to develop a Big Data project aiming to solve a problem that calls a decision; 3) Describes a particular, logical, requirements-like approach that explains how a company develops a Big Data analytics project to support decision-making.

scholarly journals An inventory of biodiversity data sources for conservation monitoring

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0242923
Author(s):  
P. J. Stephenson ◽  
Carrie Stengel
Keyword(s):  
Decision Making ◽  
Biodiversity Loss ◽  
Data Availability ◽  
Data Sources ◽  
Data Sets ◽  
Biodiversity Data ◽  
Policy Makers ◽  
The People ◽  
Science Community ◽  
Global Data

Many conservation managers, policy makers, businesses and local communities cannot access the biodiversity data they need for informed decision-making on natural resource management. A handful of databases are used to monitor indicators against global biodiversity goals but there is no openly available consolidated list of global data sets to help managers, especially those in high-biodiversity countries. We therefore conducted an inventory of global databases of potential use in monitoring biodiversity states, pressures and conservation responses at multiple levels. We uncovered 145 global data sources, as well as a selection of global data reports, links to which we will make available on an open-access website. We describe trends in data availability and actions needed to improve data sharing. If the conservation and science community made a greater effort to publicise data sources, and make the data openly and freely available for the people who most need it, we might be able to mainstream biodiversity data into decision-making and help stop biodiversity loss.

Environmental event materiality and decision making

2012 ◽  
Vol 27 (3) ◽  
pp. 284-298 ◽  
Author(s):  
Jeffrey Faux
Keyword(s):  
Decision Making ◽  
Research Approach ◽  
Policy Makers ◽  
Content Type ◽  
Environmental Event ◽  
User Groups ◽  
Environmental Events ◽  
A Company ◽  
Action Decision

PurposeThe purpose of this paper is to investigate environmental event materiality and user decision making, providing an empirical basis for reporting entities disclosures regarding material environmental events that further users' ability to make decisions.Design/methodology/approachA vignette describing an environmental event facing a company was provided to participants who were asked whether the event was deemed to be material and, second, whether the event would initiate an action or no action decision. The use of an experimental approach reveals results regarding the decision‐making process of users rather than relying on respondents stating preferences.FindingsResults indicate that user groups consider the environmental event to be material at a threshold of 6 percent. The determination of the event as material results in a “no action” decision that suggests isolated events of this size may not result in “action” decisions. The study has implications for policy makers and entities disclosing environmental events.Research limitations/implicationsThe experimental research approach adopted is primarily limited by the specific contextual nature of the event.Originality/valueEntity reporting of environmental events is receiving unprecedented levels of interest and this paper contributes to the materiality research and practice in this area.

scholarly journals Predicting Student Performance by Using Data Mining Methods for Classification

2013 ◽  
Vol 13 (1) ◽  
pp. 61-72 ◽  
Author(s):  
Dorina Kabakchieva
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Student Performance ◽  
High Potential ◽  
University Management ◽  
Mining Methods ◽  
Predicting Student Performance ◽  
Using Data ◽  
Initial Results ◽  
Support Decision Making

Abstract Data mining methods are often implemented at advanced universities today for analyzing available data and extracting information and knowledge to support decision-making. This paper presents the initial results from a data mining research project implemented at a Bulgarian university, aimed at revealing the high potential of data mining applications for university management.

scholarly journals INTERDISCIPLINARY APPLICATION OF ALGORITHMS FOR DATA MINING

2013 ◽  
Vol 3 (2) ◽  
pp. 6-9
Author(s):  
Jasmin Malkić ◽  
◽  
Nermin Sarajlić ◽  
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Clinical Trials ◽  
Information Processing ◽  
Computer Science ◽  
Statistical Significance ◽  
Active Role ◽  
Medical Treatments ◽  
To Receive

Interdisciplinary application of data mining is linked with the ability to receive and process the large amounts of data. Although even the first computers could help in executing the tasks that required accuracy and reliability atypical to the human way of information processing, only increasing the speed of computer processors and advances in computer science have introduced the possibility that computers can play a more active role in decision making. Applications of these features are found in medicine, where data mining is used in clinical trials to determine the factors that influence health, and examine the effectiveness of medical treatments. With its ability to detect patterns and similarities within the data, data mining can help determine the statistical significance, pointing to the complex combinations of factors that cause certain effect. Such approach opens the opportunities of deeper analysis than it is the case with reliance solely on statistics.

scholarly journals Implementasi Metode Clustering DBSCAN pada Proses Pengambilan Keputusan

2015 ◽  
pp. 185
Author(s):  
Anindya Santika Devi ◽  
I Ketut Gede Darma Putra ◽  
I Made Sukarsa
Keyword(s):  
Decision Making ◽  
Spatial Data ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Decision Making Process ◽  
Number Of Clusters ◽  
Potential Customer ◽  
A Company ◽  
Support Decision Making

Spatial Data Clustering is one of the significant techniques in data mining which used to obtain information or knowledge in a large number of spatial data from various applications. One technique that being a pioneer in the development of spatial data clustering algorithm is DBSCAN. This study is focused on implementation of DBSCAN method in decision making process in order to help a company to decide its potential customer. The trial results in this study show that DBSCAN method has been successfully conduct clustering process to support decision making process in determination of potential customer by forming several number of clusters.

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