scholarly journals Implementasi Data Warehouse dan Data Mining: Studi Kasus Analisis Peminatan Studi Siswa

2011 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Eka Miranda
Keyword(s):  
Data Mining ◽  
Academic Achievement ◽  
Decision Making ◽  
Data Analysis ◽  
Data Warehouse ◽  
Ad Hoc ◽  
Data Distribution ◽  
Program Selection ◽  
Mining Design ◽  
The University

This paper discusses the implementation of data mining and their role in helping decision-making related to students’ specialization program selection. Currently, the university uses a database to store records of transactions which can not directly be used to assist analysis and decision making. Based on these issues then made the data warehouse design used to store large amounts of data and also has the potential to gain new data distribution perspectives and allows to answer the ad hoc question as well as to perform data analysis. The method used consists of: record analysis related to students’ academic achievement, designing data warehouse and data mining. The paper’s results are in a form of data warehouse and data mining design and its implementation with the classification techniques and association rules. From these results can be seen the students’ tendency and pattern background in choosing the specialization, to help them make decisions. 

scholarly journals Data Warehouse, Data Mining Dan Konsep Cross-Selling Pada Analisis Penjualan Produk

2010 ◽  
Vol 1 (2) ◽  
pp. 344
Author(s):  
Eka Miranda ◽  
Natalya Elfreida
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Data Warehouse ◽  
Ad Hoc ◽  
Data Warehousing ◽  
Problem Analysis ◽  
Literature Study ◽  
Product Analysis ◽  
Huge Amount ◽  
Design And Testing

This paper is about designing and implementing data warehousing and data mining, along with their roles in supporting decision-making related to sales product analysis in cross-selling concept of PT XYZ. The database the company used is not supporting data analysis and decision-making. Therefore, it made a data warehousing design that could be used to keep data in a huge amount and could give report and answer from user’s questions in ad hoc. The method is used to design and implement data warehousing and data mining which consists of literature study, company problem analysis, and data warehousing design, and testing result. The writing results are a data warehousing design and data mining and also the implementation of cross-selling concept to analysis the sales, purchases, and customers’ cancellation data. The data could be showed and analyzed from some point of views that could help managers to analyse and acknowledge more information. 

Integrating OLAP/SOLAP in E-Business Domains

2011 ◽  
Vol 24 (3) ◽  
pp. 45-60
Author(s):  
Ben Ali ◽  
Samar Mouakket
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Data Analysis ◽  
United Arab Emirates ◽  
Decision Makers ◽  
Job Seekers ◽  
Job Market ◽  
Analysis Techniques ◽  
Analytical Processing ◽  
User Friendly

E-business domains have been considered killer domains for different data analysis techniques. Most researchers have examined data mining (DM) techniques to analyze the databases behind E-business websites. DM has shown interesting results, but this technique presents some restrictions concerning the content of the database and the level of expertise of the users interpreting the results. In this paper, the authors show that successful and more sophisticated results can be obtained using other analysis techniques, such as Online Analytical Processing (OLAP) and Spatial OLAP (SOLAP). Thus, the authors propose a framework that fuses or integrates OLAP with SOLAP techniques in an E-business domain to perform easier and more user-friendly data analysis (non-spatial and spatial) and improve decision making. In addition, the authors apply the framework to an E-business website related to online job seekers in the United Arab Emirates (UAE). The results can be used effectively by decision makers to make crucial decisions in the job market of the UAE.

scholarly journals SISTEM DATA WAREHOUSE DAN DATA MINING SEBAGAI PENGUKUR KINERJA ENTERPRISE

CCIT Journal ◽  
2011 ◽  
Vol 4 (2) ◽  
pp. 172-184
Author(s):  
Henderi Henderi ◽  
Untung Rahardja ◽  
Muhammad Yusup
Keyword(s):  
Data Mining ◽  
Life Cycle ◽  
Data Warehouse ◽  
Ad Hoc ◽  
System Development ◽  
Transaction Processing ◽  
Design System ◽  
Study Analysis ◽  
System Study ◽  
Analysis Design

Sistem informasi pada organisasi sebagian besar digunakan untuk membantu pelaksanaan business process enterprise. Sistem tersebut pada umumnya belum dapat menyediakan informasi strategis dan membantu manajemen dalam melakukan evaluasi kinerja enterprise. Hal ini terjadi karena sistem informasi sebagian besar dibangun menggunakan konsep database OLTP (online transaction processing) dan bersifat ad hoc. Permasalahan ini terjadi pula pada sistem informasi di sebagian besar Perguruan Tinggi di Kota Tangerang sebagai sampel penelitian. Alternatif pemecahan masalah tersebut adalah membangun sistem informasi yang menerapkan konsep dan cara kerja data warehouse dan data mining yang dapat dijasikan sebagai tools pengukur kinerja enterprise. Metodologi pengembangan sistemnya menggunakan metode system developmnet life cycle (SDLC). Metode SDLC terdiri dari tahapan: system study, analysis design, system development, dan implementation. Melalui pendekatan ini diciptakan sebuah sistem informasi dengan konsep data warehouse dan data mining yang dapat menghasilkan informasi yang bersifat strategis, sesuai kebutuhan, dan sebagai tools melaksanakan pengukuran kinerja enterprise. Hasil akhir penelitian adalah sebuah sistem data warehouse dan data mining sebagai tools pengukur kinerja enterprise pada Perguruan Tinggi Raharja sebagai prototipe penerapannya.

scholarly journals Introvert and extrovert personality: Is it correlated with academic achievement of Physical Education, Health and Recreation students at university level?

2021 ◽  
Vol 6 (2) ◽  
pp. 140-146
Author(s):  
Hasanuddin Jumareng ◽  
Wolter Mongsidi ◽  
Edi Setiawan ◽  
Ihsan Abdul Patah ◽  
Adi Rahadian ◽  
...  
Keyword(s):  
Academic Achievement ◽  
Data Analysis ◽  
Physical Education ◽  
Descriptive Statistics ◽  
Student Academic Achievement ◽  
High Category ◽  
University Level ◽  
Level Of Significance ◽  
Education Psychology ◽  
The University

Psychological elements, namely introverted and extroverted personalities, are currently one of the keys to determining success in academics at the University level, but it is not clear which type is more correlated with academic achievement. The purpose of this study is to investigate the correlation between introverted and extroverted personalities with student academic achievement. The subjects in this study come from the PJKR Department of the University of Suryakancana (N=20) and Halu Oleo (N=20). Data analysis uses SPSS version 25 to find descriptive statistics, normality, data linearity and person product moment correlation. The level of significance used is 0.05. The results of the study finds that introverted personality is significantly correlated with academic achievement in the high category (r= 0.749**), and extroverted personality is not significantly correlated with academic achievement (r= -0.120). Thus, it can be concluded that the personality of students with introverted type is far superior to extroverts in academic achievement at the University level. The contribution in this study contributes to knowledge in the field of physical education psychology, so that later lecturers can pay more attention and optimize academic achievement in students with both types, especially the extroverted type.

scholarly journals A Literature Survey on Data Mining Approach to Effectively Handle Cancer Treatment

2018 ◽  
Vol 7 (2.7) ◽  
pp. 729 ◽  
Author(s):  
Sri Hari Nallamala ◽  
Siva Kumar Pathuri ◽  
Dr Suvarna Vani Koneru
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Data Analysis ◽  
Cancer Treatment ◽  
User Interaction ◽  
Medical Practitioner ◽  
Vital Role ◽  
Healthcare Management ◽  
Clinical Approach ◽  
Internet Resource

The effective treatment of cancer is not very easy since diagnosis of cancer involves many stages of treatment with gradually changing lifestyles. Physicians play vital role in identifying the correct cause and feel ambiguity for making perfect decisions about hundreds of data available from the internet resource. IDA (Intelligent Data Analysis) which is a part from Data Mining techniques is quiet useful to most of the physicians for decision making about types of cancers. IDA facilitates physicians to classify, detect and analyze the cancer outcome to patients. Healthcare Management System also aids the practitioners to practically search, analyze and compare the result analysis of the patient with existing data in the HMS and guide proper treatment to the cancer affected patient. Health care data analysis comprises enormous data with diversity of health information. One among the most important points that pull down the practitioner’s confidence is that utility of latest software and most sophisticated computing machines. This put them in to the state of confusion for proper and elegant decision making for treating the cancer affected patients. Problems in user interaction, lack of awareness in data mining, improper knowledge in electronic guidelines makes physicians to work with old methods of treatment. Traditional medical practicing and modern methods of computing do not match either because of ignorance. IDA and HMS have significant impact for cancer treatment with speedy diagnosis and faster recovery. This also shows great impact on costs, clinical outcomes and proper guidelines for clinical approach. The prime motto of this survey article is to analyze the survey application, bring out the importance of comparison strategies of IDA to improve decision making for medical practitioner for effective cancer treatment.  

Medidor de agua para sistema inteligente de control de agua potable

2019 ◽  
pp. 6-12
Author(s):  
Daniel Sustaita-Cruces ◽  
Elsa Verónica Martínez-Mejía
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Big Data ◽  
Water Consumption ◽  
Public And Private ◽  
The North ◽  
Research Activities ◽  
The Many ◽  
The University ◽  
Technological University

Objectives: Develop an electronic prototype that respects the environment, through the use of microcontroller cards such as Arduino, Nodemcu, IoT concept and cloud for the control and measurement of water consumption. Objectives specific: Process the data provided by sensors and meters distributed in the main water supplies of the university, as well as the public and private sectors and combine them with information, such as consumption patterns, to build a sophisticated image of how the water network is behaving . through the concept of IoT, data mining, big data and cloud. Measure and evaluate the impacts of the results obtained by the smart meter and that provide the applications for the correct decision making. Objectives Methodologycs: Strengthen the research line of the work team, which sets the standard for us as part of an academic body, of proposing projects based on Internet of Things, Big-Data and data mining technologies, using as a platform the potential of the microcontroller boards (Photon, Arduino, etc.), to enter the new industrial model 4.0 - environment. Create an academic body that carries out research activities through the use of different research methods for the solution to different problems within the institution. Contribution The water resource is essential for life, however, society rarely thinks about the different ways of use that is generally given, or the many activities of daily life in which it is present, and how our life It would change if its availability were near the end of its life cycle. The present project arises from the need to have a better control in the water consumption registers mainly in the Technological University of the North of Guanajuato which is the place where this research originates and from these registers allow an analysis of the data of consumption with greater accuracy and the best decision making.

An Extraction, Transformation, and Loading Tool Applied to a Fuzzy Data Mining System

2011 ◽  
pp. 53-76
Author(s):  
Ramón A. Carrasco ◽  
Miguel J. Hornos ◽  
Pedro Villar ◽  
María A. Aguilar
Keyword(s):  
Data Mining ◽  
Data Warehouse ◽  
Information And Communication Technologies ◽  
Query Language ◽  
Heterogeneous Data ◽  
Mining System ◽  
Information And Communication ◽  
Semantically Heterogeneous ◽  
Integration Problems ◽  
The University

In this chapter, we address the problem of integrating semantically heterogeneous data (including data expressed in natural language), which are collected from various questionnaires published in different websites, into a Data Warehouse. We present an extension of the sentences and architecture of data mining Fuzzy Structured Query Language as an extraction, transformation, and loading tool to integrate semantically heterogeneous data from these websites. Moreover, we show a case study using the questionnaires (carried out during several years) about the courses on Information and Communication Technologies which are taught in the Business Studies implanted at the University of Granada (Spain). With this integrated information, the Data Warehouse user can make several analyses with the benefit of an easy linguistic interpretability. The solution proposed here can be used to similar integration problems.

Symbolic Data Clustering

2011 ◽  
pp. 1087-1091 ◽  
Author(s):  
Edwin Diday ◽  
M. Narasimha Murthy
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Data Analysis ◽  
Internal Structure ◽  
Data Clustering ◽  
Large Datasets ◽  
Complex Data ◽  
Symbolic Data Analysis ◽  
New Techniques ◽  
Symbolic Data

In data mining, we generate class/cluster models from large datasets. Symbolic Data Analysis (SDA) is a powerful tool that permits dealing with complex data (Diday, 1988) where a combination of variables and logical and hierarchical relationships among them are used. Such a view permits us to deal with data at a conceptual level, and as a consequence, SDA is ideally suited for data mining. Symbolic data have their own internal structure that necessitates the need for new techniques that generally differ from the ones used on conventional data (Billard & Diday, 2003). Clustering generates abstractions that can be used in a variety of decision-making applications (Jain, Murty, & Flynn, 1999). In this article, we deal with the application of clustering to SDA.

scholarly journals Data Analysis and Data Mining: Current Issues in Biomedical Informatics

2011 ◽  
Vol 50 (06) ◽  
pp. 536-544 ◽  
Author(s):  
M. Diomidous ◽  
I. N. Sarkar ◽  
K. Takabayashi ◽  
A. Ziegler ◽  
A. T. McCray ◽  
...  
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Data Analysis ◽  
Knowledge Integration ◽  
Biomedical Informatics ◽  
Data Driven ◽  
Biomedical Data ◽  
Translational Bioinformatics ◽  
Biomedical Sciences ◽  
Mining Methods

SummaryBackground: Medicine and biomedical sciences have become data-intensive fields, which, at the same time, enable the application of data-driven approaches and require sophisticated data analysis and data mining methods. Biomedical informatics provides a proper interdisciplinary context to integrate data and knowledge when processing available information, with the aim of giving effective decision-making support in clinics and translational research.Objectives: To reflect on different perspectives related to the role of data analysis and data mining in biomedical informatics. Methods: On the occasion of the 50th year of Methods of Information in Medicine a symposium was organized, which reflected on opportunities, challenges and priorities of organizing, representing and analysing data, information and knowledge in biomedicine and health care. The contributions of experts with a variety of backgrounds in the area of biomedical data analysis have been collected as one outcome of this symposium, in order to provide a broad, though coherent, overview of some of the most interesting aspects of the field.Results: The paper presents sections on data accumulation and data-driven approaches in medical informatics, data and knowledge integration, statistical issues for the evaluation of data mining models, translational bioinformatics and bioinformatics aspects of genetic epidemiology.Conclusions: Biomedical informatics represents a natural framework to properly and effectively apply data analysis and data mining methods in a decision-making context. In the future, it will be necessary to preserve the inclusive nature of the field and to foster an increasing sharing of data and methods between researchers.

scholarly journals Data mining as a tool in privacy-preserving data publishing

2010 ◽  
Vol 45 (1) ◽  
pp. 151-159 ◽  
Author(s):  
Michal Sramka
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Data Analysis ◽  
Privacy Preserving ◽  
Data Publishing ◽  
Sensitive Information ◽  
Private Data ◽  
Privacy Preserving Data Publishing

ABSTRACTMany databases contain data about individuals that are valuable for research, marketing, and decision making. Sharing or publishing data about individuals is however prone to privacy attacks, breaches, and disclosures. The concern here is about individuals’ privacy-keeping the sensitive information about individuals private to them. Data mining in this setting has been shown to be a powerful tool to breach privacy and make disclosures. In contrast, data mining can be also used in practice to aid data owners in their decision on how to share and publish their databases. We present and discuss the role and uses of data mining in these scenarios and also briefly discuss other approaches to private data analysis.

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