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%.

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 Penyelesaian Masalah Pengelolaan Lumbung Pangan Desa Menggunakan Case-Based Reasoning dengan Algoritma K-Nearest Neighbor

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Mgs. Afriyan Firdaus ◽  
Dwi Rosa Indah ◽  
Putri Eka Sevtiyuni ◽  
Choirunnisa Qonitah
Keyword(s):  
Problem Solving ◽  
Nearest Neighbor ◽  
Technical Problem ◽  
Case Based Reasoning ◽  
Test Results ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
Existing Problems ◽  
K Nearest Neighbor Algorithm ◽  
Case Based

In this paper, we discuss the problem solving of village food barn management using Case-Based Reasoning (CBR) with the K-Nearest Neighbor algorithm. This research was carried out by adopting the stages of the CBR cycle and the nearest neighbor algorithm. The results of the study show that the application of CBR and K-nearest neighbor algorithms can support the resolution of knowledge problems in village food barn management using technical problem solving based on the symptoms and solutions to existing problems. Based on the test results, the problem-solving accuracy was 92%.Keywords - case-based reasoning, K-nearest neighbor, food barn, problem-solving

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 Rancang Bangin Case Based Reasoning untuk Diagnosis Hama dan Penyakit Tanaman Nilam menggunakan Nearest Neighbor Kombinasi Certainty Factor

2020 ◽  
Vol 7 (3) ◽  
pp. 477
Author(s):  
Rabiah Adawiyah ◽  
Fitrianti Handayani
Keyword(s):  
Nearest Neighbor ◽  
Raw Material ◽  
Case Based Reasoning ◽  
Certainty Factor ◽  
Accuracy Rate ◽  
Pests And Diseases ◽  
System Diagnosis ◽  
Case Study Site ◽  
Case Based

<p class="Judul2" align="left"> </p><p>Tanaman nilam menghasilkan minyak nilam (<em>patchouli oil</em>) yang digunakan sebagai bahan baku kosmetik, parfum, antiseptik, sabun, obat, dan insektisida. Dalam pengembangan dan peningkatannya tanaman nilam mengalami beberapa kendala seperti serangan hama dan penyakit yang mengakibatkan rendahnya hasil panen khususnya pada daerah Desa Gunung Sari Kecamatan Watubangga Kabupaten Kolaka. Pengembangan tanaman nilam yang terserang hama dan penyakit seringkali terhambat karena masih banyak petani yang tidak mengetahui jenis hama dan penyakit yang menyerang tanaman petani. Oleh sebab itu sistem pakar berbasis kasus atau <em>Case Based Reasoning (CBR)</em> dibangun untuk mendiagnosis jenis hama dan penyakit tanaman nilam. Pada penelitian ini digunakan 7 jenis penyakit dan 22 gejala berdasarkan studi kasus tempat penelitian. CBR menggunakan metode<em> similarity</em> Nearest Neighbor untuk menemukan kemiripan antar kasus yang berada dalam tahapan <em>retrieve</em>. Pada penelitian ini digunakan juga metode lain yaitu Certainty Factor yang berfungsi untuk mengetahui derajat kepercayaan terhadap hasil diagnosis sistem dalam menghasikan jenis hama dan penyakit tanaman nilam. Berdasarkan hasil penelitian dengan menggunakan kombinasi dua metode Nearest Neighbor dan Certainty factor maka dihasilkan sistem mampu melakukan diagnosis hama dan penyakit tanaman nilam dengan nilai <em>similarity</em> 0.7 dan tingkat kepercayaaan sebesar 97,2 %  serta menghasilkan tingkat akurasi sistem sebesar 93.82 % dan tingkat kesalahan sistem 3 %</p><div><p> </p><p><em><strong>Abstract</strong></em></p><p><em>Patchouli oil is used as a raw material for cosmetics, perfumes, antiseptics, soaps, medicines, and insecticides. In the development and improvement of patchouli plants experienced several obstacles such as pests and diseases which resulted in low yields, especially in the area of Gunung Sari Village, Watubangga District, Kolaka Regency. The development of patchouli plants attacked by pests and diseases is often hampered because there are still many farmers who do not know the types of pests and diseases that attack farmers' crops. Therefore a case based reasoning (CBR) expert system was built to diagnose patchouli plants and pests. In this study 7 types of diseases were used and 22 symptoms were based on the case study site. CBR uses the similarity Nearest Neighbor method to find similarities between cases that are in the retrieval stage. In this study, another method is used, namely Certainty Factor, which functions to determine the degree of trust in the results of system diagnosis in producing patchouli species and diseases. Based on the results of the study by using a combination of the two Nearest Neighbor and Certainty factor methods, the system was able to diagnose patchouli pests and diseases with a similarity value of 0.7 and a confidence level of 97.2% and produce a system accuracy rate of 93.82% and a system error rate of 3%</em></p><p><em><strong><br /></strong></em></p></div>

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.

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