scholarly journals Case Base Reasoning Untuk Mendiagnosis Penyakit Hipertensi Menggunakan Metode Indexing Density Based Spatial Clustering Application With Noise (DBSCAN)

2019 ◽  
Vol 7 (1) ◽  
pp. 88-100
Author(s):  
Herdiesel Santoso
Keyword(s):  
Euclidean Distance ◽  
Spatial Clustering ◽  
Case Based Reasoning ◽  
Minkowski Distance ◽  
Unhealthy Lifestyle ◽  
Distance Method ◽  
Threshold Score ◽  
Case Base Reasoning ◽  
Minimum Points ◽  
Case Based

Abstract. Hypertension is one of the health problems priority in the world because of the increasing of life expectancy and an unhealthy lifestyle. Many people with hypertension are unreachable and undiagnosed by a health worker and they do not do treatment according to the health recommendation. The Case-Based Reasoning (CBR) Method can be applied to solve the new cases in diagnosed hypertension using the answer or experience from the old case by comparing the new case and the old case. In order to do not use all the basic case data for finding a similar case, it makes an indexing process is needed. The DBSCAN algorithm implementation as indexing method is expected to improve the time and memory efficiency in CBR, especially during the retrieval process. The result of the CBR test with the cluster-indexing has a better accuracy and time process than the non-indexing CBR. The minimum parameter points and epsilon that has been chosen for clustering on hypertension data case is the combination of epsilon score 9 and minimum points score 3 with the silhoutte coefficient score 0.240 and average cluster time 0.541 seconds. The Minkowski Distance method has better accuracy than the Euclidean Distance method because by the threshold score ≥ 0.9 the CBR system with the Minkowski distance method is able to diagnose the disease with 100 % accuracy and the average best retrieval time, it is 0.0586 second Abstrak. Hipertensi menjadi salah satu prioritas masalah kesehatan di dunia karena peningkatan angka harapan hidup dan gaya hidup yang tidak sehat. Banyak penderita hipertensi yang tidak terjangkau dan terdiagnosis oleh tenaga kesehatan serta tidak menjalani pengobatan sesuai anjuran kesehatan. Metode Case-Based Reasoning (CBR) dapat diaplikasikan untuk menyelesaikan masalah baru dalam diagnosis penyakit hipertensi menggunakan jawaban atau pengalaman dari masalah lama  dengan membandingkan kasus baru dengan kasus lama. Supaya proses pencarian kasus yang mirip tidak perlu melibatkan seluruh data pada basis kasus,maka diperlukan proses indexing. Implementasi algoritme DBSCAN sebagai metode indexing diharapkan dapat meningkatkan efisiensi waktu dan memori pada CBR khususnya ketika proses retrival. Hasil pengujian CBR dengan cluster-indexing memiliki akurasi dan waktu proses yang lebih baik dari pada CBR non-indexing. Parameter minimum points dan epsilon yang dipilih untuk melakukan clustering pada data kasus penyakit hipertensi adalah kombinasi epsilon 9 dan minimum points 3 dengan nilai silhoutte coeffisien 0.240 dan waktu klaster rata-rata 0.541 detik. Metode minkowski distance memiliki akurasi yang lebih baik dari pada metode euclidean distance, karena dengan threshold ≥ 0.9 sistem CBR dengan metode minkowski distance mampu mendignosis penyakit dengan akurasi 100% dan waktu retrieve rata-rata terbaik yaitu 0.0586 detik.

scholarly journals Implementasi Case Base Reasoning Untuk Mendiagnosa Penyakit Gigi dan Mulut

Techno Com ◽  
2017 ◽  
Vol 16 (1) ◽  
pp. 70-79
Author(s):  
Fryda Fatmayati ◽  
Kusrini ◽  
Emha Taufiq Lutfi
Keyword(s):  
Knowledge Representation ◽  
Euclidean Distance ◽  
Case Based Reasoning ◽  
Jaccard Coefficient ◽  
Tanimoto Coefficient ◽  
Case Base ◽  
Case Base Reasoning ◽  
Case Based

Penyakit gigi dan mulut dapat dialami oleh semua orang mulai dari anak-anak hingga dewasa.Namun karena biaya berobat ke dokter gigi yang mahal maka masyarakat enggan memeriksanakan keluhannya terutama pada masyarakat kalangan menengah ke bawah. Padahal jika penyakit gigi dan mulut tidak segera dirawat akan bertambah parah. Case-Based Reasoning meniru kemampuan manusia, yaitu menyelesaikan masalah baru menggunakan jawaban atau pengalaman dari masalah lama.Penyajian pengetahuan (knowledge representation) dibuat dalam bentuk kasus-kasus (case).Setiap kasus berisi masalah dan jawaban, sehingga kasus lebih mirip dengan suatu pola tertentu.Cara kerja Case-Based Reasoning adalah dengan membandingkan kasus baru dengan kasus lama. Jika tidak ada yang cocok maka Case-Based Reasoning akan melakukan adaptasi, dengan cara memasukkan kasus baru tersebut ke dalam database penyimpanan kasus (case base), sehingga secara tidak langsung pengetahuan CBR akan bertambah. Tujuan dari penelitian ini, yaitu mengetahui kemiripan kasus baru dan kasus lama dengan penerapan Case-Based Reasoning (CBR) dan membandingkan dua metode yang digunakan, yaitu Extended Jaccard Coefficient (Tanimoto Coefficient) dan Euclidean Distance similarity dengan memilih hasil akurasi terbaik dari kedua metode tersebut. Hasil pengujian terhadap data uji penyakit gigi dan mulut menunjukkan sistem memiliki unjuk kerja dengan tingkat akurasi menggunakan metode Extended Jaccard Coefficient sebesar 95.24% dan Euclidean Distance Similarity sebesar 100%.   Kata kunci—Case Base Reasoning, Extended Jaccard Coefficient, Euclidean Distance Similarity, penyakit gigi dan mulut 

scholarly journals APLIKASI CASE BASED REASONING UNTUK IDENTIFIKASI SERANGAN HAMA PADA TANAMAN JERUK

Transmisi ◽  
2018 ◽  
Vol 20 (3) ◽  
pp. 96
Author(s):  
Esi Putri Silmina ◽  
Retantyo Wardoyo
Keyword(s):  
Euclidean Distance ◽  
Case Based Reasoning ◽  
Case Base ◽  
Case Base Reasoning ◽  
Case Based

Tanaman jeruk adalah tanaman buah tahunan yang berasal dari ASIA. Pembudidayaan tanaman jeruk dipengaruhi oleh berbagai faktor yaitu, teknik budidaya, kondisi lingkungan serta serangan hama dan penyakit. Dari ketiga faktor tersebut yang sampai sekarang menjadi masalah adalah gangguan hama dan penyakit. Rendahnya produktivitas tanaman jeruk disebabkan oleh serangan hama. Penelitian ini akan mengidentifikasi serangan hama pada tanaman jeruk dengan cara menerapkan Sistem Case Based Reasoning. Perhitungan similaritas yang digunakan dalam sistem Case Base Reasoning adalah metode Euclidean Distance. Hasil penelitian ini menunjukkan Sistem Case Based Reasoning ini dapat digunakan untutk membantu user mengidentifikasi hama yang menyerang tanaman jeruk. Problem baru dikatakan similar (mirip) 100% dengan kasus yang lama apabila nilai similaritas dari d(p,q) sama dengan 1 sedangkan tidak  similar apabila nilai d(p,q) sama dengan 0. Nilai similaritas antara 0 sampai dengan 1. 

scholarly journals Local Triangular Kernel-Based Clustering (LTKC) for Case Indexing on Case-Based Reasoning

2018 ◽  
Vol 12 (2) ◽  
pp. 139
Author(s):  
Damar Riyadi ◽  
Aina Musdholifah
Keyword(s):  
Heart Disease ◽  
Thyroid Disease ◽  
Euclidean Distance ◽  
Manhattan Distance ◽  
Case Based Reasoning ◽  
Minkowski Distance ◽  
Retrieval Time ◽  
Accuracy Comparison ◽  
Speed Up ◽  
Case Based

This study aims to improve the performance of Case-Based Reasoning by utilizing cluster analysis which is used as an indexing method to speed up case retrieval in CBR. The clustering method uses Local Triangular Kernel-based Clustering (LTKC). The cosine coefficient method is used for finding the relevant cluster while similarity value is calculated using Manhattan distance, Euclidean distance, and Minkowski distance. Results of those methods will be compared to find which method gives the best result. This study uses three test data: malnutrition disease, heart disease, and thyroid disease. Test results showed that CBR with LTKC-indexing has better accuracy and processing time than CBR without indexing. The best accuracy on threshold 0.9 of malnutrition disease, obtained using the Euclidean distance which produces 100% accuracy and 0.0722 seconds average retrieval time. The best accuracy on threshold 0.9 of heart disease, obtained using the Minkowski distance which produces 95% accuracy and 0.1785 seconds average retrieval time. The best accuracy on threshold 0.9 of thyroid disease, obtained using the Minkowski distance which produces 92.52% accuracy and 0.3045 average retrieval time. The accuracy comparison of CBR with SOM-indexing, DBSCAN-indexing, and LTKC-indexing for malnutrition diseases and heart disease resulted that they have almost equal accuracy.

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 PENALARAN BERBASIS KASUS UNTUK MENDIAGNOSA PENYAKIT INFEKSI MENULAR SEKSUAL (IMS) MENGGUNAKAN ALGORITMA WEIGHTED EUCLIDEAN DISTANCE

2020 ◽  
Vol 8 (2) ◽  
pp. 133-138
Author(s):  
Derin N Liu ◽  
Derin N Liu ◽  
Sebastianus A Mola ◽  
Yelly Y Nabuasa
Keyword(s):  
Sexually Transmitted Infections ◽  
Euclidean Distance ◽  
Patient Treatment ◽  
Case Based Reasoning ◽  
Transmitted Infection ◽  
Distance Method ◽  
Sexually Transmitted ◽  
Average Accuracy ◽  
Total Data ◽  
Case Based

Case-based reasoning is a methodology for solving problems by utilizing previous experience. In this study the authors apply case-based reasoning to diagnose sexually transmitted infection using the weighted Euclidean distance method. Source of the knowledge base was obtained by collecting medical record of patients with sexually transmitted infections in 2016-2017. The process of finding a solution starts with eliminating irrelevant data using the C4.5 method and continues with the calculation of the similarity value using the Weighted Euclidean Distance algorithm. This system can diagnose 5 types of sexually transmitted infections based on 123 existing symptoms. System result in the form of sexually transmitted infections based on symptoms experienced by the patient, treatment solution and presentation of similarities between new cases and old cases. Based on the result of testing with 127 cases of sexually transmitted infections obtained result: testing uses the K-Fold Cross Validation scenario, the total data is divided into 10fold and the testing process is divided into 2 parts, namely testing using indexing and testing without using indexing. For testing using the highest accuracy indexing obtained at 90.84% in the second fold, and the average accuracy of the entire fold is 88.55% with the average time generated 9498 ms (millisecond), while testing without using the highest accuracy indexing obtained by 63.03% in the second fold, and the average accuracy of the entire fold is 53.48% with the average time generated 9975 ms (millisecond).  

scholarly journals SISTEM PAKAR DIAGNOSA PENYAKIT AYAM PETELUR MENGGUNAKAN METODE CASE BASED REASONING BERBASIS WEB

2019 ◽  
Vol 3 (2) ◽  
pp. 126-132
Author(s):  
Zendy Achmad Faisal
Keyword(s):  
Economic Impact ◽  
Newcastle Disease ◽  
Case Based Reasoning ◽  
Technical Service ◽  
Infectious Bronchitis ◽  
Case Base ◽  
Case Base Reasoning ◽  
Gumboro Disease ◽  
Case Based

Munculnya permasalahan dan penyakit pada ayam ini disinyalir akibat kelalaian peternak yang kurang memperhatikan nutrisi bahan pakan yang diberikan pada ayam peliharaannya. Penyakit-penyakit yang sering menjangkit ayam petelur adalah: Newcastle Disease (ND), Infectious Bronchitis (IB), Gumboro Disease dan Flu. Pada setiap penyakit tersebut memiliki gejala yang hampir sama namun membutuhan penanganan dan tindakan yang bebeda-beda sehingga banyak peternak yang sulit mengidentifikasi penyakit apa yang menjangkit ternak mereka.Pengumpulan data yang dijadikan bahan pembuatan sistem pakar menggunakan metode case base reasoning ini dilakukan dengan wawancara dengan technical service obat (ahli dalam bidang penanganan penyakit ayam petelur) pada instansi Manunggal Putra Unggas. Dalam tahap ini, berkonsultasi tentang informasi mengenai segala penyakit ayam petelur, gejala penyakit ayam petelur, serta bobot nilai pada setiap gejala yang merupakan tingkat keyakinan dari ahli dalam penyakit ayam petelur. Setelah dilakukan wawancara, maka diperoleh informasi mengenai mengenai nilai bobot dari penyakit dan gejala penyakit ayam yang akan digunakan dalam sistem pakar diagnosis penyakit pada ayam petelur yang diperoleh dari technical service penanganan unggas yaitu Bpk Taufan Rohadie.Pada jurnal hasil penelitian sosio-economic impact didapatkan pada insutri peternakan ayam yang ada di Indonesia bahwa wabah penyakit ayam pada umumnya menyerang perusahaan peternakan ayam petelur. Sekitar 83% dari total populasi. Informasi ini mengungkapkan bahwa perusahaan ayam petelur lebih rentan terkena wabah penyakit daripada perusahaan ayam boiler.

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 Geometric Case Based Reasoning for Stock Market Prediction

2020 ◽  
Vol 12 (17) ◽  
pp. 7124
Author(s):  
Se-Hak Chun ◽  
Young-Woong Ko
Keyword(s):  
Stock Market ◽  
Nearest Neighbors ◽  
Percentage Error ◽  
Case Based Reasoning ◽  
Stock Market Index ◽  
Distance Method ◽  
Market Index ◽  
Shape Distance ◽  
Case Based ◽  
Better Than

Case based reasoning is a knowledge discovery technique that uses similar past problems to solve current new problems. It has been applied to many tasks, including the prediction of temporal variables as well as learning techniques such as neural networks, genetic algorithms, decision trees, etc. This paper presents a geometric criterion for selecting similar cases that serve as an exemplar for the target. The proposed technique, called geometric Case Based Reasoning, uses a shape distance method that uses the number of sign changes of features for the target case, especially when extracting nearest neighbors. Thus, this method overcomes the limitation of conventional case-based reasoning in that it uses Euclidean distance and does not consider how nearest neighbors are similar to the target case in terms of changes between previous and current features in a time series. These concepts are investigated against the backdrop of a practical application involving the prediction of a stock market index. The results show that the proposed technique is significantly better than the random walk model at p < 0.01. However, it was not significantly better than the conventional CBR model in the hit rate measure and did not surpass the conventional CBR in the mean absolute percentage error.

Sistem Pendiagnosa Kerusakan Pada Motor Menggunakan Metode Case-Base Reasoning Approach

2019 ◽  
Vol 2 (1) ◽  
pp. 72-79
Author(s):  
Jevan Nelson ◽  
Septian Dicky Chandra
Keyword(s):  
Case Based Reasoning ◽  
Turn On ◽  
Attribute Weight ◽  
Simple Matching Coefficient ◽  
Case Base ◽  
Case Base Reasoning ◽  
Case Based

This paper proposes one of approach for diagnosing faults in motorcycle by using Case-Based Reasoning Approach (CBR). CBR process through four stages consist of Retrieve, Reuse, Revise and Retain. The calculation of equation have been done by Simple Matching Coefficient (SMC). Diagnosing faults in motorcycle was started by tracking initial indication that occur in the motorcycle, then ended when the solution has been found with similar of existing case. The result shown that the most often problem after diagnosis was the machine unable to start/difficult to turn on with the Carburetor Attribute Weight achieve the highest percentage of attributes. Keyword: CBR, Diagnosis, Motorcycle, SMC

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