scholarly journals Penerapan Metode Ripple Down Rules Untuk Mendiagnosa Penyakit Burung

2020 ◽  
Vol 1 (2) ◽  
pp. 89
Author(s):  
Dahri Musnandar
Keyword(s):  
Expert System ◽  
Knowledge Acquisition ◽  
Normal Condition ◽  
Clinical Symptoms ◽  
Inference Search ◽  
Ripple Down Rules

Birds or poultry are members of vertebrate animals that have feathers and wings. Diseased birds certainly look different from their normal condition and exhibit strange symptoms, if they are usually agile and active or often chirping, but when they are sick the bird looks limp, and chooses more silence. Of the symptoms that arise there are symptoms that can be seen by the eye or clinically and by looking at these symptoms can be known what diseases attack birds. To get a solution to the disease, a tool or system is needed to do it. The Ripple Down Rule (RDR) method is one method that has expert system inference / search capabilities and knowledge acquisition. By using the RDR method a system will be able to infer or identify several types of diseases suffered by birds as experts with clinical symptoms such as input. Recognized disease data adjusts rules (rules) that are made to be able to match the symptoms of bird disease stored in the system.

scholarly journals Penerapan Metode Ripple Down Rules Untuk Mendiagnosa Penyakit Hamster

2020 ◽  
Vol 2 (2) ◽  
pp. 71-75
Author(s):  
Sri Putri Sundari
Keyword(s):  
Artificial Intelligence ◽  
Expert System ◽  
Health Workers ◽  
Clinical Symptoms ◽  
Animal Health ◽  
Fields Of Study ◽  
Inference Search ◽  
The Many ◽  
Ripple Down Rules ◽  
Over Time

Hamsters are one of the many animals that are owned and are beginning to attract many people. Over time, hamsters began to be known as pets. Hamasters are also susceptible to disease. Diseases in hamsters and symptoms that cause very much, the problem now is not only need to know the cause of the disease but the important thing is to know quickly the disease suffered is also overcome so that the disease can be treated. Expert system which is one branch of artificial intelligence, capable of acting as experts in certain fields of study, researchers think animal health workers to help diagnose disease in hamsters as early as possible. The Ripple Down Rule (RDR) method is one method that has expert system inference / search capabilities and knowledge acquisition. By using RDR a system will be able to identify the disease as an expert with clinical symptoms such as input. The expert system for diagnosing disease in hamsters uses the Ripple Down Rules (RDR) method to explore the symptoms displayed in the form of questions in order to diagnose the type of hasmter's illness, to get results for treatment and to cure hasmter disease.

scholarly journals Knowledge Acquisition and Representation for High-Performance Building Design: A Review for Defining Requirements for Developing a Design Expert System

2021 ◽  
Vol 13 (9) ◽  
pp. 4640
Author(s):  
Seung-Yeoun Choi ◽  
Sean-Hay Kim
Keyword(s):  
Decision Support ◽  
Expert System ◽  
Expert Systems ◽  
Knowledge Acquisition ◽  
Data Analytics ◽  
High Performance ◽  
Problem Definition ◽  
Design Expert ◽  
Decision Support Process ◽  
High Performance Building

New functions and requirements of high performance building (HPB) being added and several regulations and certification conditions being reinforced steadily make it harder for designers to decide HPB designs alone. Although many designers wish to rely on HPB consultants for advice, not all projects can afford consultants. We expect that, in the near future, computer aids such as design expert systems can help designers by providing the role of HPB consultants. The effectiveness and success or failure of the solution offered by the expert system must be affected by the quality, systemic structure, resilience, and applicability of expert knowledge. This study aims to set the problem definition and category required for existing HPB designs, and to find the knowledge acquisition and representation methods that are the most suitable to the design expert system based on the literature review. The HPB design literature from the past 10 years revealed that the greatest features of knowledge acquisition and representation are the increasing proportion of computer-based data analytics using machine learning algorithms, whereas rules, frames, and cognitive maps that are derived from heuristics are conventional representation formalisms of traditional expert systems. Moreover, data analytics are applied to not only literally raw data from observations and measurement, but also discrete processed data as the results of simulations or composite rules in order to derive latent rule, hidden pattern, and trends. Furthermore, there is a clear trend that designers prefer the method that decision support tools propose a solution directly as optimizer does. This is due to the lack of resources and time for designers to execute performance evaluation and analysis of alternatives by themselves, even if they have sufficient experience on the HPB. However, because the risk and responsibility for the final design should be taken by designers solely, they are afraid of convenient black box decision making provided by machines. If the process of using the primary knowledge in which frame to reach the solution and how the solution is derived are transparently open to the designers, the solution made by the design expert system will be able to obtain more trust from designers. This transparent decision support process would comply with the requirement specified in a recent design study that designers prefer flexible design environments that give more creative control and freedom over design options, when compared to an automated optimization approach.

scholarly journals IDENTIFIKASI PENYAKIT PADA TERUMBU KARANG MENGGUNAKAN RIPPLE DOWN RULES

2018 ◽  
Vol 1 (2) ◽  
pp. 165-174
Author(s):  
Agus Cahyo Nugroho
Keyword(s):  
Coral Reef ◽  
Expert System ◽  
Expert Systems ◽  
Specific Problem ◽  
Specific Knowledge ◽  
Web Based ◽  
Human Thinking ◽  
Ripple Down Rules

Along with the development of technology, people developed a system that capable of adopting processes and human thinking as an expert system that contains specific knowledge so that everyone can use it to solve a specific problem, namely the diagnosis of coral reef disease. The purpose of this study is to develop an expert system for diagnosing coral reef disease  in the form of websites using PHP with a MySQL database. Expert system for diagnosing coral reef disease problem is using Ripple Down Rules (RDR) method has a goal to discover symptoms that appear in the form of questions that can diagnose the coral reef disease based on website. Web based expert system is able to recognize types of coral reef disease after consultation by answering a few questions that are displayed by the application of expert systems and can infer some types of coral  reef disease. Data coral reef disease that already known adapt to rules which are made for matching the symptoms of coral reef disease.

A Generalized Expert System Shell for Implementing Mechanical Design Applications: Review, Introduction and Fundamental Concepts

1988 ◽  
Author(s):  
P. Premkumar ◽  
S. N. Kramer
Keyword(s):  
Expert System ◽  
Knowledge Acquisition ◽  
Mechanical Design ◽  
Research Effort ◽  
Design Evaluation ◽  
Expert System Shell ◽  
Reactive Planning

Abstract The foundations for an expert system shell for implementing mechanical design applications are presented in this paper. The shell supports facilities for knowledge acquisition, quasi-reactive planning, design evaluation, and subjective explanation. The underlying philosophy of each of these facilities and some preliminary implementation issues are discussed. A brief summary of a recent research effort and its implications on the development of a generalized expert system shell for implementing mechanical design applications are also presented.

Ripple Down Rules

2011 ◽  
pp. 207-226 ◽  
Author(s):  
Debbie Richards
Keyword(s):  
Expert Systems ◽  
Knowledge Acquisition ◽  
Clinical Decision Support System ◽  
First Generation ◽  
Clinical Decision ◽  
Commercial Success ◽  
Combined Use ◽  
Representation Technique ◽  
History Of ◽  
Ripple Down Rules

Knowledge is becoming increasingly recognized as a valuable resource. Given its importance it is surprising that expert systems technology has not become a more common means of utilizing knowledge. In this chapter we review some of the history of expert systems, the shortcomings of first generation expert systems, current approaches and future decisions. In particular we consider a knowledge acquisition and representation technique known as Ripple Down Rules (RDR) that avoids many of the limitations of earlier systems by providing a simple, user-driven knowledge acquisition approach based on the combined use of rules and cases and which support online validation and easy maintenance. RDR has found particular commercial success as a clinical decision support system and we review what features of RDR make it so suited to this domain.

Knowledge Acquisition for Expert System Design

1993 ◽  
pp. 25-51 ◽  
Author(s):  
Spyros Tzafestas ◽  
Alex Adrianopoulos
Keyword(s):  
Expert System ◽  
Knowledge Acquisition ◽  
System Design

Differential Learning Expert System in Data Management

2009 ◽  
pp. 597-604
Author(s):  
R. Manjunath
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Data Management ◽  
Knowledge Acquisition ◽  
User Interfaces ◽  
High Performance ◽  
Evolutionary Model ◽  
Human Expert ◽  
Incremental Development ◽  
Differential Learning

Expert systems have been applied to many areas of research to handle problems effectively. Designing and implementing an expert system is a difficult job, and it usually takes experimentation and experience to achieve high performance. The important feature of an expert system is that it should be easy to modify. They evolve gradually. This evolutionary or incremental development technique has to be noticed as the dominant methodology in the expert-system area. The simple evolutionary model of an expert system is provided in B. Tomic, J. Jovanovic, & V. Devedzic, 2006. Knowledge acquisition for expert systems poses many problems. Expert systems depend on a human expert to formulate knowledge in symbolic rules. The user can handle the expert systems by updating the rules through user interfaces (J. Jovanovic, D. Gasevic, V. Devedzic, 2004). However, it is almost impossible for an expert to describe knowledge entirely in the form of rules. An expert system may therefore not be able to diagnose a case that the expert is able to. The question is how to extract experience from a set of examples for the use of expert systems.

Using the Internet for Knowledge Acquisition in Expert Systems Development: A Case Study

1996 ◽  
Vol 11 (3) ◽  
pp. 223-234
Author(s):  
Kathleen K. Molnar ◽  
Ramesh Sharda
Keyword(s):  
Expert System ◽  
Knowledge Acquisition ◽  
Electronic Mail ◽  
System Development ◽  
Cost Savings ◽  
Lessons Learned ◽  
The Internet ◽  
Domain Experts ◽  
Use Of The Internet ◽  
Multiple Experts

Knowledge acquisition is a major task in expert system development. This paper proposes one way of acquiring knowledge for expert system development: through the use of the Internet. Internet resources (e.g. Usenet groups, ListServ discussion lists, archive sites and on-line literature/database searches) are knowledge sources. Internet tools such as newsreaders, electronic mail, Telnet, FTP, gophers, archie, WAIS and World Wide Web provide access to these sources. The results of an exploratory study that examined the use of the Internet as a knowledge source are presented here in conjunction with a framework for using the Internet in the planning phase. Four major advantages can be found in this: the availability of multiple experts in multiple domains, the interaction of domain experts and end users, time/cost savings, and convenience. The lessons learned and some additional issues are also presented.

Sign in / Sign up

Export Citation Format

Share Document