scholarly journals Metode Modified Weighted Minkowski Untuk Pengembangan Sistem Penalaran Berbasis Kasus

2017 ◽  
Vol 7 (1) ◽  
pp. 1
Author(s):  
Edi Faizal
Keyword(s):  
Data Collection ◽  
Knowledge Acquisition ◽  
Error Rate ◽  
Case Based Reasoning ◽  
Test Results ◽  
Research Focus ◽  
Accuracy Rate ◽  
Different Levels ◽  
Case Based ◽  
Representation Of Knowledge

Knowledge acquisition process is not easy, because of the different levels of expertise even though all true. Computer experts had tried other methods to resolve the problem of the acquisition, which is known as case-based reasoning. Representation of knowledge in CBR is a collection of previous case. This research focus is the application of CBR for diagnosing womb diseases. The level of similarity is calculated by using the modified weighted Minkowski. Methods of data collection are interviews, observation and study of literature. The test results show the system can be recognize the womb disease correctly is 94.44% (sensitivity), specitifity rate of 57.14%, PPV of 85.00% and 80.00% NPV. The system have an accuracy rate of 84.00% with an error rate of 16.00%.

scholarly journals Error repair and knowledge acquisition via case-based reasoning

1997 ◽  
Vol 91 (1) ◽  
pp. 85-101 ◽  
Author(s):  
Takeshi Kohno ◽  
Susumu Hamada ◽  
Dai Araki ◽  
Shoichi Kojima ◽  
Toshikazu Tanaka
Keyword(s):  
Knowledge Acquisition ◽  
Case Based Reasoning ◽  
Error Repair ◽  
Case Based

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 Pembuatan Aplikasi Diagnosa Kerusakan Mesin Sepeda Motor Matic dengan Case-Based Reasoning

2015 ◽  
Vol 2 (3) ◽  
pp. 192
Author(s):  
Sandy Kosasi
Keyword(s):  
Intelligent System ◽  
Automatic Transmission ◽  
Case Based Reasoning ◽  
Test Results ◽  
Design Development ◽  
System Validation ◽  
Wheeled Vehicle ◽  
System Maintenance ◽  
Motorcycle Engine ◽  
Case Based

Sepeda motor matic sebagai terobosan baru kendaraan roda dua dengan transmisi otomatis memberikan implikasi kepada sistem perawatannya. Jumlah mekanik yang terbatas dan minimnya pengetahuan pengguna menyebabkan berbagai kesulitan dalam perawatannya khususnya dalam mengatasi kerusakan mesin. Pembuatan aplikasi sistem cerdas melalui metode case-based reasoning dapat memberikan kemudahan melakukan diagnosis awal secara mandiri. Case-based reasoning memiliki kemampuan dapat memberikan hasil diagnosis yang lebih akurat berdasarkan kejadian terdahulu dan dapat direvisi kembali dalam memecahkan permasalahan terbaru. Metode perancangan aplikasinya menggunakan reuse-based yang meliputi enam tahap yaitu spesifikasi persyaratan, analisis komponen, modifikasi persyaratan, integrasi design sistem dengan reuse, pengembangan dan integrasi, serta validasi sistem. Tujuan penelitian untuk melakukan diagnosa kerusakan mesin sepeda motor matic dan memberikan solusi awal mengenai kondisi kerusakan dan pencegahannya melalui media situs web. Hasil pengujian memperlihatkan aplikasi ini memiliki kemampuan mendiagnosa kerusakan dan memberikan solusi penyelesaian masalah dari pengguna dengan rata-rata nilai similaritas antara 0,62 dan 0,7 dengan nilai keakuratan solusi dari pakar sebesar 80% dan 90%. Automatic motorcycles as a new breakthrough of two-wheeled vehicle with an automatic transmission have implications for the system maintenance. A limited number of mechanics and lack of users’ knowledge cause many difficulties in treatment, especially in dealing with the engine damage. The Design of the intelligent system through case-based reasoning method can provide easiness of initial diagnosis independently. Case-based reasoning has the ability to provide more accurate diagnosis results based on the previous events and may be revised to solve the latest problems. The design of application uses a reuse-based method that includes six stages: requirements specification, component analysis, modification of the terms, integration with reuse system design, development and integration, and system validation. The purpose of the research is for diagnosing automatic motorcycle engine damage and provide an initial solution on its condition and prevention through the medium of the website. The test results demonstrate that this application has the ability to diagnose the damage and provide problem solving solutions to the users with an average of similarity value between 0,62 and 0,7 with an accuracy value of expert solutions for 80% and 90%.

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

Knowledge acquisition tool for case-based reasoning s ystems

1995 ◽  
Vol 9 (2) ◽  
pp. 201-212 ◽  
Author(s):  
A.I. Mechitov ◽  
H.M. Moshkovich ◽  
D.L. Olson ◽  
B. Killingsworth
Keyword(s):  
Knowledge Acquisition ◽  
Case Based Reasoning ◽  
Case Based
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