scholarly journals Seleksi Penyerang Utama Menggunakan K-Means Clustering Dan Sistem Pendukung Keputusan Metode Topsis

2017 ◽  
Vol 2 (1) ◽  
pp. 37-48
Author(s):  
Renenata Ardilesmana Siregar
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Selection Process ◽  
Ideal Solution ◽  
Major Player ◽  
The Right ◽  
Selection Of ◽  
The Ideal

Untuk  menentukan penyerang ideal dalam sepak bola agar sesuai karakter dan kriteria yang diharapkan,  diperlukan  pelatih  yang  mempunyai naluri  tajam  dan  juga  sistem  yang  bisa membantu pelatih dalam memberikan pilihan. Biasanya dalam proses penentuan pemain masih dilakukan  secara  manual dengan melihat dari karakter dan kriteria dari pemain tersebut. Tetapi terkadang hanya dengan melihat dari karakter dan kriteria dari pemain tersebut saja masih kurang cukup sehingga jauh dari apa yang diharapkan. Untuk  mempermudah dalam pemilihan penyerang ideal, maka diperlukan suatu sistem yang dapat membantu pelatih untuk  memilih penyerang yang dibutuhkan sesuai dengan kebutuhan tim yaitu dengan menggunakan teknik K-Means Clustering dalam metode data mining sebagai proses dalam menyeleksi pemain untuk bergabung  dalam  suatu  tim  dan  juga  didukung  dengan  metode  Sistem  Pendukung Keputusan (Decision Support Systems) The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) sebagai proses dalam menentukan penyerang yang akan bermain sebagai pemain utama dalam tim yang menggunakan beberapa kriteria untuk  memilih pemain yang tepat. Dengan hasil penelitian ini, diharapkan dapat membantu pelatih dalam proses seleksi pemain dan dapat mengubah cara penilaian terhadap sifat subjektif agar lebih obyektif dalam pengambilan keputusan. Kata Kunci :Data Mining, K-Means Clustering, Sistem Pendukung Keputusan To  determine  the  ideal  attacker  in football to match the expected character and criteria, a  coach who has a sharp instinct and a system that can assist the coach in providing choices. Usually  in  the  process  of  determining  the  player  is  still  done  manually  by  look ing  at  the characters  and  criteria  of  the  player.  But  sometimes just by look ing at the characters and criteria of the player is still not enough so far from what is expected. To facilitate the selection of ideal attackers, a system that can help the trainer to select the attacker needed according to the needs of the team is by using K-Means Clustering technique in the method of data mining as a process in selecting players to join a team and also supported by Decision Support Systems method The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is the process of determining which attack er will play as a major player in the team using multiple criteria to select the right player. With the results of this study, it is expected to assist trainers in the selection process of players and can change the way the assessment of the subjective nature to be more objective in decision making. Keywords: Data Mining, K-Means Clustering, Decision Support System.  

scholarly journals Decision Support System For Selecting Bali Tourist Attractions Using The PROMETHEE Method

SinkrOn ◽  
2019 ◽  
Vol 3 (2) ◽  
pp. 1
Author(s):  
Eva Zuraidah
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Recommendation System ◽  
Support Systems ◽  
Descriptive Analysis ◽  
Tourist Attraction ◽  
Tourist Attractions ◽  
The Right ◽  
Selection Of

Tourism is currently very potential to be developed as a source of local income by providing information both online and offline to the community so that regional income increases. Bali is one of the tourist destinations in Indonesia which is visited by many local and foreign tourists. The island of Bali has many interesting sights consisting of natural attractions, royal festivals, culinary tours, traditional markets, and museums. There are many criteria that must be considered, so through this recommendation system, tourists can find out what places are in Bali they will visit. One of the problems of decision making with many criteria and attributes in the selection of attractions is to provide detailed decisions that refer to the scale of weight possessed. Decision support systems provide priority results of attractions that are suitable for every traveler. Traveling is very important because with tourism we can eliminate fatigue due to activity during the day. The selection of the right tourist attraction also affects this, so it is necessary to choose the right tourist attraction. This study focused on the application of multi-attribute decision making (MADM) to decision support systems (SPK) using preferred organizational methods for enrichment and evaluation (Promeethe). When a traveler fills out a questionnaire, he must be consistent with the answers to get the best results based on his willingness and characteristics. This research uses descriptive analysis method that presents a summary of the results of surveys and interviews of tourists who want to choose Bali tourist attractions according to costs, security, natural beauty, facilities, and infrastructure and location.

A Theory of Hierarchy-Gray for Purchase Decision Support Systems

2014 ◽  
Vol 624 ◽  
pp. 681-686
Author(s):  
Gui Qiang Wang ◽  
Xiao Wei Fan ◽  
Xiao Ning Wang ◽  
He Bing Chen ◽  
Wei Chen ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Supplier Selection ◽  
Support Systems ◽  
Comprehensive Evaluation ◽  
Evaluation Index System ◽  
Mathematics Model ◽  
Gray Theory ◽  
Selection Of ◽  
The Ideal

The selection of suppliers is very important in the purchase of decision support systems. In this paper, firstly the complicated factors of supplier selection which existed in the course of purchase inviting bid are analyzed systemically, Then an comprehensive evaluation index system for picking up the ideal bidders is put forward, Based on the hierarchy-gray theory, a multi-attribute decision mathematics model of purchase inviting bid is constructed, Finally feasibility and effectiveness of the model is testified by a example.

scholarly journals Decision Support, Knowledge Representation and Management: A Balancing Act between Clinical Use and Implementation of Sophisticated Reasoning Techniques?

2007 ◽  
Vol 16 (01) ◽  
pp. 87-89
Author(s):  
B. Brigl ◽  
Keyword(s):  
Decision Support ◽  
Knowledge Representation ◽  
Decision Support Systems ◽  
Support Systems ◽  
Selection Process ◽  
Clinical Use ◽  
Clinical Environments ◽  
Complex Decision ◽  
The One ◽  
Selection Of

SummaryTo position the papers selected for the IMIA Yearbook 2007 in the context of current research in decision support, knowledge representation and management.Synopsis of the articles selected for the IMIA Yearbook 2007.In the Yearbook 2007 the best paper selection of the section Decision support, Knowledge management in Representation’ shows, that the evaluation of the influence of decision support on medical behavior and outcome is as important as research on new reasoning technologies and methods.The best paper selection process shows on the one hand that there is still a deep gap between rather small decision support solutions successfully evaluated in clinical environments and more complex decision support systems using sophisticated reasoning techniques, but lack of clinical use. On the other hand the implementation of decision support systems today benefits from research done in the last decades.

Student Admission Assesment using Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA)

2018 ◽  
Author(s):  
Rivalri Kristianto Hondro ◽  
Mesran Mesran ◽  
Andysah Putera Utama Siahaan
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Selection Process ◽  
Initial Step ◽  
Optimization Method ◽  
Ratio Analysis ◽  
Multi Objective Optimization ◽  
Prospective Students ◽  
Multi Objective

Procurement selection process in the acceptance of prospective students is an initial step undertaken by private universities to attract superior students. However, sometimes this selection process is just a procedural process that is commonly done by universities without grouping prospective students from superior students into a class that is superior compared to other classes. To process the selection results can be done using the help of computer systems, known as decision support systems. To produce a better, accurate and objective decision result is used a method that can be applied in decision support systems. Multi-Objective Optimization Method by Ratio Analysis (MOORA) is one of the MADM methods that can perform calculations on the value of criteria of attributes (prospective students) that helps decision makers to produce the right decision in the form of students who enter into the category of prospective students superior.

Decision Support Systems in Crop Protection

1994 ◽  
Vol 23 (4) ◽  
pp. 281-285 ◽  
Author(s):  
Jonathan D. Knight ◽  
John D. Mumford
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Crop Protection ◽  
Expert Advice ◽  
Management Decision ◽  
Sufficient Information ◽  
Record Keeping ◽  
Pests And Diseases ◽  
The Right

All farmers and growers have at some time faced the decision of whether to control a pest in their crop. In order to make the correct decision the farmer needs access to, and an understanding of, sufficient information relevant to such pest problems. Decision support systems are able to help farmers make these difficult decisions by providing information in an easily understandable and quickly accessed form. The increasing use of computers by farmers for record-keeping and business management is putting the hardware necessary for the implementation of these systems onto more and more farms. The scarcity of expert advice, increasingly complex decisions and reduced economic margins all increase the importance of making the right pest management decision at the right time. It is against this background that decision support systems have an important role to play in the fight against losses caused by pests and diseases.

Business Intelligence and Data Mining

2013 ◽  
pp. 57-70
Author(s):  
Zsolt T. Kardkovács
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Strategic Planning ◽  
Decision Support Systems ◽  
Business Intelligence ◽  
Business Processes ◽  
Support Systems ◽  
Decision Makers ◽  
Key Factors ◽  
Short Introduction

Whenever decision makers find out that they want to know more about how the business works and progresses, or why customers do what they do, then data miners are summoned, and business intelligence is to be built or altered. Data mining aims at retrieving valid, interesting, explicable connection between key factors for either operative reporting or supporting strategic planning. While data mining discovers static connections between factors, business intelligence visualizes relevant data for decision makers in order to make them identify fast changes and analyze precisely business states. In this chapter, the authors give a short introduction for data oriented decision support systems with data mining and business intelligence in it. While these techniques are widely used in business processes, there are much more bad practices than good ones. We try to make an attempt to demystify and clear the myths about these technologies, and determine who should and how (not) to use them.

scholarly journals Benefits and Limitations of Decision Support Systems (DSS) with a Special Emphasis on Weeds

Agronomy ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 548 ◽  
Author(s):  
Panagiotis Kanatas ◽  
Ilias S. Travlos ◽  
Ioannis Gazoulis ◽  
Alexandros Tataridas ◽  
Anastasia Tsekoura ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Weed Management ◽  
Crop Production ◽  
Support Systems ◽  
Management Strategies ◽  
Farming Systems ◽  
Climatic Conditions ◽  
Management Options ◽  
The Right

Decision support systems (DSS) have the potential to support farmers to make the right decisions in weed management. DSSs can select the appropriate herbicides for a given field and suggest the minimum dose rates for an herbicide application that can result in optimum weed control. Given that the adoption of DSSs may lead to decreased herbicide inputs in crop production, their potential for creating eco-friendly and profitable weed management strategies is obvious and desirable for the re-designing of farming systems on a more sustainable basis. Nevertheless, it is difficult to stimulate farmers to use DSSs as it has been noticed that farmers have different expectations of decision-making tools depending on their farming styles and usual practices. The function of DSSs requires accurate assessments of weeds within a field as input data; however, capturing the data can be problematic. The development of future DSSs should target to enhance weed management tactics which are less reliant on herbicides. DSSs should also provide information regarding weed seedbank dynamics in the soil in order to suggest management options not only within a single period but also in a rotational view. More aspects ought to be taken into account and further research is needed in order to optimize the practical use of DSSs for supporting farmers regarding weed management issues in various crops and under various soil and climatic conditions.

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