scholarly journals Comparison of Case-Based Reasoning and Dempster Shafer on Expert System of Cassava Disease Identification

2018 ◽  
Vol 215 ◽  
pp. 01004
Author(s):  
Minarni ◽  
Indra Warman ◽  
Yuhendra ◽  
Wenda Handayani
Keyword(s):  
Expert System ◽  
Nearest Neighbor ◽  
Performance Comparison ◽  
Identification Accuracy ◽  
Base Case ◽  
Case Based Reasoning ◽  
Web Based ◽  
Identification Process ◽  
Disease Identification ◽  
Case Based

This study aims to develop a web-based expert system that can identify cassava disease using Case-Based Reasoning Method and Shafer Dempster Method, and to know the performance comparison of both based on the accuracy of identification. Case-Based Reasoning (CBR) is a computer-generated system that uses old knowledge to solve new problems. CBR provides solutions to new cases by looking at the oldest cases that are closest to new cases. The identification process is done by entering new cases containing the symptoms to be identified into the system, then perform the process of calculating the value of similarity between the new case and the base case using the nearest neighbor method. Dempster Shafer based on two ideas is the idea of obtaining degrees of confidence of subjective possibilities and the rule of dempster safer itself to combine degrees of confidence based on the evidence obtained. This expert system is built using PHP programming language and MySQL data base. The output of the system is the percentage of identification result of both methods. Testing and analysis results show that Case-Based Reasoning provides better identification accuracy than Dempster Shafer.

scholarly journals Online Expert Systems For Bamboo Identification Using Case Based Reasoning

2017 ◽  
Vol 7 (5) ◽  
pp. 2766 ◽  
Author(s):  
Endina Putri Purwandari ◽  
Ariefa Primair Yani ◽  
Ramanda Sugraha ◽  
Kurnia Anggriani ◽  
Endang Widi Winarni
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Expert Knowledge ◽  
High Accuracy ◽  
Identification Accuracy ◽  
Base Case ◽  
Case Based Reasoning ◽  
Bamboo Species ◽  
Input System ◽  
Case Based

Bamboo is a typical plant that thrives in tropical countries like Indonesia. The diversity of bamboo species makes difficult to classified, then requiring the expertise from specialist who understands deeply about bamboo characteristics. The paper purpose to (1) adopts the bamboo expert knowledge into bamboo criteria of expertise; (2) implementing Case Based Reasoning method in online expert system for bamboo identification in Bengkulu Province; and (3) determined the identification accuracy of bamboo using expert systems. The system uses Case Based Reasoning with four main steps: retrieve, reuse, revise, and retain. Bamboo expert identify bamboo criteria into 6 morphology, 31 features, and 219 attributes as an input system. The results showed that the Case Based Reasoning method has high accuracy for identifying the bamboo species and can solve the problem of bamboo identification as a new case based on old case that stored in the base case.

The Study of Hybrid Gaussian CBR Technique and Using in Camshaft Design System

2010 ◽  
Vol 108-111 ◽  
pp. 603-607
Author(s):  
Wei Yan ◽  
Xue Qing Li ◽  
Xu Guang Tan ◽  
De Hui Tong ◽  
Qi Gao
Keyword(s):  
Expert System ◽  
Decision Model ◽  
Nearest Neighbor ◽  
Design System ◽  
Case Based Reasoning ◽  
K Nearest Neighbor ◽  
Engine Design ◽  
Cam Profile ◽  
Aided Design ◽  
Case Based

In this paper, we propose a hybrid decision model using case-based reasoning augmented the Gaussian and k nearest neighbor methods for aided design camshaft in engine. The hybrid Gaussian k-NN (HGKNN) CBR scheme is designed to compute memberships between cam profile and engine parameters, which provides a more flexible and practical mechanism for reusing the decision knowledge. These methods were implemented in the database application and expert system following the examples of Cam Profile. To get the designed case, the retrieved results were compared and analyzed by HGKNN and k-NN algorithm in the CBR database. It proves the validity of HGKNN and CBR design system is used successfully in engine design process.

scholarly journals Analisis Penyakit Pada Tanaman Cabai Dengan Metode Case Based Reasoning Berbasis Web

2021 ◽  
Vol 1 (1) ◽  
pp. 43-48
Author(s):  
Desi Ernawati ◽  
Riki Andri Yusda ◽  
Guntur Maha Putra
Keyword(s):  
Expert System ◽  
Plant Diseases ◽  
Case Based Reasoning ◽  
Good Care ◽  
Web Based ◽  
Case Base ◽  
Case Base Reasoning ◽  
Case Based

Abstract:Chili is a production cropthatis much needed by the  community. Good care is needed to increase the production of chili plants. Production of chili plants will decrease if the types of diseases that attack are not considered. To find out about chili plant diseases, farmers only look at the disease without knowing the symptoms that appear beforehand so that it will affect the production of chili plants.So that we need experts who understand the symptoms of disease in chili plants.The existence of experts can be replaced by a system designed to detect symptoms of disease in chili plants.The expert system to be designed is web-based using the case-based reasoning method.This expert system is expected to help increase the productivity of chili plants.            Keywords:expert system; chili; case-based reasoning; chili plants.  Abstrak:Cabai merupakan tanaman produksi yang banyak dibutuhkan oleh masyarakat. Untuk meningkatkan produksi tanaman cabai diperlukan perawatan yang baik. Produksi dari tanaman cabai akan menurun jika tidak diperhatikan jenis penyakit yang menyerang. Untuk mengetahui penyakit tanaman cabai para petani hanya melihat penyakitnya saja tanpa mengetahui terlebih dahulu gejala yang muncul sehingga akan mempengaruhi hasil produksi tanaman cabai. Sehingga diperlukan pakar yang mengerti mengenai gejala penyakit pada tanaman cabai. Keberadaan pakar bisa digantikan oleh sebuah sistem yang dirancang untuk mendeteksi gejala penyakit pada tanaman cabai. Sistem pakar yang akan dirancang berbasis web dengan menggunakan metode case base reasoning. Sistem pakar ini nantinya diharapkan membantu untuk peningkatan produktivitas tanaman cabai. Kata kunci:sistem pakar; cabai; casebasereasoning; tanaman cabai.

scholarly journals Expert System Diagnosis of Bowel Disease Using Case Based Reasoning with Nearest Neighbor Algorithm

2017 ◽  
Vol 4 (2) ◽  
pp. 134-142 ◽  
Author(s):  
Lucky Gagah Vedayoko ◽  
Endang Sugiharti ◽  
Much Aziz Muslim
Keyword(s):  
Expert System ◽  
Medical Record ◽  
Bowel Disease ◽  
Nearest Neighbor ◽  
Case Based Reasoning ◽  
Nearest Neighbor Algorithm ◽  
System Diagnosis ◽  
Source Case ◽  
System Data ◽  
Case Based

Expert System is a computer system that has been entered the base of knowledge and set of rules to solve problems like an expert. One method in the expert system is Case Based Reasoning. To strengthen the retrieve stage of this method, the Nearest Neighbor algorithm is used. Bowel is one of the digestive organs susceptible to disease. The purpose of this study is to implement expert systems using Case Based Reasoning with Nearest Neighbor algorithm in diagnosing bowel disease and determine the accuracy of the system. Data used in this research are 60 data, obtained from medical record RSUD dr. Soetrasno Rembang. Variables used are general symptoms and types of diseases. The level of system accuracy resulting from scenario are 40 data as source case, and 20 data as target case that is equal to 95%.

scholarly journals SISTEM PAKAR DIAGNOSA PENYAKIT JANTUNG DENGAN METODE CASE BASED REASONING (CBR)

2021 ◽  
Vol 3 (1) ◽  
pp. 1-12
Author(s):  
Dona Dona ◽  
Hendri Maradona ◽  
Masdewi Masdewi
Keyword(s):  
Decision Making ◽  
Heart Disease ◽  
Expert System ◽  
Programming Languages ◽  
Case Based Reasoning ◽  
Web Based ◽  
Heart Patients ◽  
Entity Relationship ◽  
Entity Relationship Diagram ◽  
Case Based

Heart disease is a disease that is very dangerous for human survival, therefore it must be addressed early on from the appearance of symptoms. Advances in expert systems can overcome this problem by designing a web-based computer system that uses databases and programming languages ​​such as PHP-MySQL so that it can help heart patients to diagnose the disease. The purpose of this research is to build a web-based expert system for diagnosing heart disease. Expert system application in this decision making uses the Case Based Reasoning (CBR) method, namely the method of making decisions by comparing new cases with old cases through four processes of retrieve, reuse, revise, and retain. Analysis and system design used are Context Diagram, Data FlowDiagram, Entity Relationship Diagram and Flowchart. In this expert system, the system will giving orders in the form of selecting the symptoms experienced, namely what symptoms are experienced. Then the patient selects the symptoms experienced by checking the symptoms experienced. The system will provide diagnostic results based on the symptoms experienced by the patient. Result diagnosis in the form of the type of heart disease experienced and the treatment solution and how much the percent chance the patient has the disease.

scholarly journals Sistem Pakar Diagnosis Penyakit Ikan Gurami (Osphronemus Goramy) Menggunakan Case Based Reasoning

2020 ◽  
Vol 7 (4) ◽  
pp. 779
Author(s):  
Adinda Rahmi Saraswati ◽  
Yudha Saintika ◽  
Afandi Nur Aziz Thohari ◽  
Ade Rahmat Iskandar
Keyword(s):  
Expert System ◽  
Nearest Neighbor ◽  
Online News ◽  
Case Based Reasoning ◽  
Fish Disease ◽  
Test Results ◽  
Bacterial Diseases ◽  
Accuracy Test ◽  
Best Value ◽  
Case Based

<p>Ikan Gurami (<em>Osphronemus Goramy)</em> merupakan ikan yang banyak dibudidayakan dan dikomsumsi masyarakat ini menjadi sektor unggulan di beberapa wilayah kabupaten Banyumas. Ikan gurami yang dibudidayakan oleh masyarakat Banyumas, sebenarnya bukan tanpa hambatan. Salah satu hambatan bagi peternak gurami adalah penyakit yang disebabkan oleh bakteri. Pada penelitian ini penulis membuat sistem pakar untuk mendiagnosis penyakit ikan Gurami yang disebabkan bakteri. Penelitian ini menggunakan metode<em> Case Based Reasoning</em> dan <em>Similarity</em> <em>Nearest Neighbor</em> untuk mendapatkan solusi yang terbaik dari kasus yang di identifikasi. Metode tersebut membandingkan antara kasus lama dengan kasus baru dan menghitung suatu nilai <em>similarity </em>kasus. Nilai <em>similarity</em> tertinggi dapat dijadikan kesimpulan untuk kasus yang paling mirip dengan diagnosa pakar. Sehingga dari kedua metode tersebut dapat dihasilkan sistem pakar yang dapat mendiagnosis dan menganalisis sesuai dengan nilai kemiripan gejala terhadap penyakit, serta menampilkan solusi penanganan dari penyakit yang didiagnosis. Hasil pengujian antar kasus dan sistem menggunakan perhitungan <em>similarity</em> mencapai nilai terbaik yaitu 100%. Hasil pengujian akurasi sistem untuk diagnosis yang sesuai dengan pikiran pakar, memperoleh hasil sebesar 93,33% dari 30 kasus yang diuji dengan sistem. Kesimpulan dari hasil tersebut adalah sistem dapat dikatakan layak untuk mendiagnosis penyakit Gurami yang disebabkan bakteri sesuai dengan yang dipikirkan pakar.</p><p> </p><p><em><strong>Abstract</strong></em></p><p><em>Gurami (Osphronemus Goramy) is a fish that is widely cultivated and consumed by the community. This fish is a leading sector in several regions of Banyumas district. Gouramy which is cultivated by the Banyumas people, is actually not without obstacles. One obstacle for gouramy breeders is a disease caused by bacteria. Reporting from the online news portal, circulating in February 2018 circulated that news about Gurami farmers was losing money because thousands of broodstock fish that had been raised to death were attacked by bacterial diseases, namely Aeromoniasis. Experts who handle this are limited, namely only 2 people in the Banyumas Regency.</em><em> </em><em>In this study the authors made an expert system to diagnose Gurami fish disease caused by bacteria. This study uses the Case Based Reasoning (CBR) and Nearest Neighbor methods used to get the best solution from the identified case. The CBR method compares the old case with the new case and calculates a case similarity value. The system was built with 13 symptoms and 3 Gurami diseases caused by bacteria. Each symptom each has a weight of 5, 3, and 1. The highest similarity value can be used as a conclusion for the case most similar to the expert diagnosis. So that from these two methods an expert system can be produced that can diagnose and analyze according to the similarity of symptoms to the disease, as well as display solutions to the treatment of diagnosed diseases. The test results are between cases and the system uses the similarity calculation to achieve the best value of 100%. The results of the system accuracy test for diagnoses that are in accordance with the expert's mind, obtained results of 93.33% from 30 cases tested with the system. The conclusion of these results is that the system can be said to be feasible to diagnose Gurami disease caused by bacteria according to what experts think.</em></p><p><em><strong><br /></strong></em></p>

Graphical Fault Diagnosis Expert System of CNC Machine Tool Based on B/S

2014 ◽  
Vol 945-949 ◽  
pp. 1707-1712
Author(s):  
Bin Shen ◽  
Shu Yu Zhao ◽  
Jia Hai Wang ◽  
Juergen Fleischer
Keyword(s):  
Fault Diagnosis ◽  
Expert System ◽  
Machine Tool ◽  
Case Based Reasoning ◽  
Cnc Machine Tool ◽  
Cnc Machine ◽  
Rule Based ◽  
Detailed Case ◽  
Case Based ◽  
Remote Fault Diagnosis

Based on the authors previous work of developing an expert system for fault diagnosis of CNC machine tool, this paper studied the theory and method of CNC remote fault diagnosis expert system based on B/S, and presents schema and structure of the expert system in detailed. Case based reasoning is used for the multi-alarm diagnosis, and rule based reasoning is used for single-alarm diagnosis. At last fault diagnosis expert system was designed and developed making use of C# and ASP.NET.

A Case Based Reasoning System For Recommendation Of Restaurant In Jimbaran Using K-Nearest Neighbor

2020 ◽  
Vol 9 (2) ◽  
pp. 267
Author(s):  
I Gede Teguh Mahardika ◽  
I Wayan Supriana
Keyword(s):  
Recommendation System ◽  
Nearest Neighbor ◽  
Case Based Reasoning ◽  
Retrieval Process ◽  
K Nearest Neighbor ◽  
Reasoning System ◽  
Human Ability ◽  
And Function ◽  
Case Based ◽  
Selection Of

Culinary is one of the favorite businesses today. The number of considerations to choose a restaurant or place to visit becomes one of the factors that is difficult to determine the restaurant or place to eat. To get the desired place to eat advice, one needs a recommendation system. Decisions made by the recommendation system can be used as a reference to determine the choice of restaurants. One method that can be used to build a recommendation system is Case Based Reasoning. The Case Based Reasoning (CBR) method mimics human ability to solve a problem or cases. The retrieval process is the most important stage, because at this stage the search for a solution for a new case is carried out. The study used the K-Nearest Neighbor method to find closeness between new cases and case bases. With the selection of features used as domains in the system, the results of recommendations presented can be more suggestive and accurate. The system successfully provides complex recommendations based on the type and type of food entered by the user. Based on blackbox testing, the system has features that can be used and function properly according to the purpose of creating the system.

Retrieving Assembly Part Design Using Case-Based Reasoning and Genetic Algorithms

2005 ◽  
Author(s):  
Guanghsu A. Chang ◽  
Cheng-Chung Su ◽  
John W. Priest
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithms ◽  
Nearest Neighbor ◽  
Similarity Measures ◽  
Experimental Results ◽  
Case Based Reasoning ◽  
Feature Weight ◽  
Similar Part ◽  
Case Base ◽  
Case Based

Artificial intelligence (AI) approaches have been successfully applied to many fields. Among the numerous AI approaches, Case-Based Reasoning (CBR) is an approach that mainly focuses on the reuse of knowledge and experience. However, little work is done on applications of CBR to improve assembly part design. Similarity measures and the weight of different features are crucial in determining the accuracy of retrieving cases from the case base. To develop the weight of part features and retrieve a similar part design, the research proposes using Genetic Algorithms (GAs) to learn the optimum feature weight and employing nearest-neighbor technique to measure the similarity of assembly part design. Early experimental results indicate that the similar part design is effectively retrieved by these similarity measures.

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