scholarly journals SISTEM PENALARAN BERBASIS KASUS UNTUK PENDUKUNG DIAGNOSIS GANGGUAN PENYAKIT PADA UNGGAS (Case Based Reasoning System to Support Diagnosis of Diseases in Poultry)

2017 ◽  
Vol 2 (1) ◽  
Author(s):  
Murien Nugraheni ◽  
Sri Hartati
Keyword(s):  
Cosine Similarity ◽  
Case Based Reasoning ◽  
Test Cases ◽  
Test Results ◽  
Reasoning System ◽  
C4.5 Algorithm ◽  
Amount Of Knowledge ◽  
Similarity Method ◽  
Case Based ◽  
Diagnosis Of Diseases

Case-Based Reasoning (CBR) is a computer system that uses old knowledge to solve new problems. CBR provide solutions for new cases by looking at an old case that comes closest to the new case. It will be very useful because it eliminates the need to extract the model as required by the rules-based system. Moreover, CBR can also be started from a small amount of knowledge, because the knowledge of CBR can be increased gradually when a case is added.This study tries to establish a system for Case-Based Reasoning System to Support Diagnosis of Diseases in Poultry by looking at the characteristics of existing symptoms in poultry. Diagnosis process is done by inserting a new cases that contain the symptoms of the disease to be diagnosed into the system, then the system will do the indexing process or classification with C4.5 algorithm method to obtain an index of new cases. After obtaining an index of the cases, then the system do the calculating of the value of similarity between the new case by case which has the same index with new cases using Cosine Similarity method. The case taken is the case with the highest similarity value. If a case is not successfully diagnosed, then the case will be revised by experts. Revised successful cases will be stored into the system to be used as new knowledge for the system.The results showed case-based reasoning system to diagnose disease of poultry can help experts and farmers in performing diagnostics. The test results of 30 test cases, system has been to produce similarity of 28 cases (93.33%) and obtained 2 cases (6.67%) have similarity values below 0.8 will be revised by experts.Keywords: CBR, poultry, indexing, similarity, cosine similarity

scholarly journals Case-Based Reasoning untuk Diagnosa Penyakit THT (Telinga Hidung dan Tenggorokan)

2013 ◽  
Vol 7 (1) ◽  
Author(s):  
Tedy Rismawan ◽  
Sri Hartati
Keyword(s):  
Case Based Reasoning ◽  
Test Cases ◽  
Test Results ◽  
Rule Based ◽  
Reasoning System ◽  
Backpropagation Method ◽  
Abstract Case ◽  
Coefficient Method ◽  
Case Based ◽  
Rule Based Systems

AbstrakCase-Based Reasoning (CBR) merupakan sistem penalaran komputer yang menggunakan pengetahuan lama untuk mengatasi masalah baru.CBR memberikan solusi terhadap kasus baru dengan melihat kasus lama yang paling mendekati kasus baru. Hal ini akan sangat bermanfaat karena dapat menghilangkan kebutuhan untuk mengekstrak model seperti yang dibutuhkan oleh sistem berbasis aturan. Penelitian ini mencoba untuk membangun suatu sistem Penalaran Berbasis Kasus untuk melakukan diagnosa penyakit THT (Telinga, Hidung dan Tenggorokan). Proses diagnosa dilakukan dengan cara memasukkan kasus baru (target case) yang berisi gejala-gejala ang akan didiagnosa ke dalam sistem, kemudian sistem akan melakukan proses indexing dengan metode backpropagation untuk memperoleh indeks dari kasus baru tersebut. Setelah memperoleh indeks, sistem selanjutnya melakukan proses perhitungan nilai similarity antara kasus baru dengan basis kasus yang memiliki indeks yang sama menggunakan metode cosine coefficient. Kasus yang diambil adalah kasus dengan nilai similarity paling tinggi. Jika suatu kasus tidak berhasil didiagnosa, maka akan dilakukan revisi kasus oleh pakar. Kasus yang berhasil direvisi akan disimpan ke dalam sistem untuk dijadikan pengetahuan baru bagi sistem. Hasil penelitian menunjukkan sistem penalaran berbasis kasus untuk mendiagnosa penyakit THT ini membantu paramedis dalam melakukan diagnosa. Hasil uji coba sistem terhadap 111 data kasus uji, terdapat 9 kasus yang memiliki nilai similarity di bawah 0.8.  Kata kunci—case-based reasoning, indexing, similarity, backpropagation, cosine coefficient Abstract Case-Based Reasoning (CBR) is a reasoning system that uses old knowledge to solve new problem. CBR provides solutions to new cases by looking at old case that comes closest to the new case. It will be very useful because it eliminates the need to extract the model as required by the rule-based systems. This studytriestoestablisha system forCBR for diagnosingdiseasesof ENT.Diagnosisprocessis done byinsertinga new casethat containsthe symptoms ofthe disease to bediagnosed, thenthe system willdo theindexingprocess with backpropagation method toobtainan indexofnewcases. Afterthat, the systemdo thecalculation of the valueof similaritybetweenthe newcasebycasebasiswhichhas thesame indexwithnew cases using cosine coefficient method. The casetaken isthe casewiththe highestsimilarityvalue. If acaseis not successfullydiagnosed, thecasewillbe revisedby theexperts and it can be used asnew knowledgefor thesystem. The results showedcase-basedreasoningsystemtodiagnosediseasesof ENTcan helpparamedicsin performingdiagnostics. The test results of 111 data test cases, obtained 9 cases that have similarity values below 0.8. Keywords—case-based reasoning, indexing, similarity, backpropagation, cosine coefficient

scholarly journals CASE-BASED REASONING PEMBERIAN REKOMENDASI PROFESI BERDASARKAN MINAT DAN BAKAT SISWA MENGGUNAKAN SIMPLE MATCHING COEFFICIENT SIMILARITY

2020 ◽  
Vol 3 (1) ◽  
pp. 35-45
Author(s):  
Made Hanindia Prami Swari ◽  
Rahel Widya Arianti ◽  
Faisal Muttaqin
Keyword(s):  
High School ◽  
High School Student ◽  
Case Based Reasoning ◽  
Simple Matching Coefficient ◽  
Reasoning System ◽  
Similarity Method ◽  
Case Basis ◽  
Case Based

One of the applications of technology in psychology is a system for determination of interest and talent systems. The system of determining interests and talents in this case can be applied in determining professional recommendations based on the interests and talents of a high school student who will continue to lectures or jobs in accordance with their fields. A person's interests and talents can be known from habits, preferences and hobbies. The Case-based Reasoning system created in this study uses cases from respondents which is collected using a questionnaire containing some questions about a person's interest in. To match new cases with old cases stored on a case basis, the authors use the Simple Matching Coefficient similarity method. The system will recommend a suitable work after the user inputs his preferences and habits. based on the results of the tests conducted, it was found that the system had produced the same calculation between the values generated by the system and manual calculations. While based on testing that conducted on data test, it was found that the system was able to provide an accuracy value of 83.33%.

scholarly journals Case-Based Reasoning untuk Diagnosis Penyakit Jantung

2017 ◽  
Vol 11 (1) ◽  
pp. 1 ◽  
Author(s):  
Eka Wahyudi ◽  
Sri Hartati
Keyword(s):  
Heart Disease ◽  
Euclidean Distance ◽  
Nearest Neighbor ◽  
Disease Diagnosis ◽  
Case Based Reasoning ◽  
Test Results ◽  
Minkowski Distance ◽  
Heart Disease Diagnosis ◽  
Similarity Method ◽  
Case Based

Case Based Reasoning (CBR) is a computer system that used for reasoning old knowledge to solve new problems. It works by looking at the closest old case to the new case. This research attempts to establish a system of CBR  for diagnosing heart disease. The diagnosis process  is done by inserting new cases containing symptoms into the system, then  the similarity value calculation between cases  uses the nearest neighbor method similarity, minkowski distance similarity and euclidean distance similarity.            Case taken is the case with the highest similarity value. If a case does not succeed in the diagnosis or threshold <0.80, the case will be revised by experts. Revised successful cases are stored to add the systemknowledge. Method with the best diagnostic result accuracy will be used in building the CBR system for heart disease diagnosis.            The test results using medical records data validated by expert indicate that the system is able to recognize diseases heart using nearest neighbor similarity method, minskowski distance similarity and euclidean distance similarity correctly respectively of 100%. Using nearest neighbor get accuracy of 86.21%, minkowski 100%, and euclidean 94.83%

scholarly journals Using extended siamese networks to provide decision support in aquaculture operations

2021 ◽  
Author(s):  
Bjørn Magnus Mathisen ◽  
Kerstin Bach ◽  
Agnar Aamodt
Keyword(s):  
Neural Networks ◽  
Decision Support ◽  
Similar Case ◽  
Decision Makers ◽  
Similarity Function ◽  
Case Based Reasoning ◽  
Similarity Learning ◽  
Reasoning System ◽  
Siamese Networks ◽  
Case Based

AbstractAquaculture as an industry is quickly expanding. As a result, new aquaculture sites are being established at more exposed locations previously deemed unfit because they are more difficult and resource demanding to safely operate than are traditional sites. To help the industry deal with these challenges, we have developed a decision support system to support decision makers in establishing better plans and make decisions that facilitate operating these sites in an optimal manner. We propose a case-based reasoning system called aquaculture case-based reasoning (AQCBR), which is able to predict the success of an aquaculture operation at a specific site, based on previously applied and recorded cases. In particular, AQCBR is trained to learn a similarity function between recorded operational situations/cases and use the most similar case to provide explanation-by-example information for its predictions. The novelty of AQCBR is that it uses extended Siamese neural networks to learn the similarity between cases. Our extensive experimental evaluation shows that extended Siamese neural networks outperform state-of-the-art methods for similarity learning in this task, demonstrating the effectiveness and the feasibility of our approach.

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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