scholarly journals Implementation Of K-Nearest Neighbor - Certainty Factor For Expert System Detection Of Idiopathic Thrombocytopenic Purpura

2021 ◽  
Vol 328 ◽  
pp. 04009
Author(s):  
Eva Y. Puspaningrum ◽  
Budi Nugroho ◽  
Dwi Putri Safira
Keyword(s):  
Expert System ◽  
Idiopathic Thrombocytopenic Purpura ◽  
Nearest Neighbor ◽  
Autoimmune Disorder ◽  
The Body ◽  
K Nearest Neighbor ◽  
Certainty Factor ◽  
Thrombocytopenic Purpura ◽  
Parameter Values ◽  
Test Scenarios

Idiopathic Thrombocytopenic Purpura (ITP) is an autoimmune disorder. ITP can occur in children and adults. This disease can be fatal because the platelet count is low due to the destruction of excessive platelets so that it can interfere with vital organs and bleeding occurs. The lack of knowledge of ordinary people about ITP disease, so many people assume that bruises and nosebleeds on the body are caused by fatigue. For that, we need a system that can imitate the expertise of an expert in diagnosing this disease based on the symptoms felt. The method used to support the expert system is the K-Nearest Neighbor and Certainty Factor methods which are a combination of 2 methods, where the classification results from the K-Nearest Neighbor method will be given a certainty value by the Certainty Factor method so as to produce a prediction. The results of combining the two methods can produce certainty in the diagnosis. Based on the test results using 3 test scenarios using parameter values k=3, k=5, k=7 and the results obtained the highest accuracy value with parameter value k=7 obtained an accuracy rate of 90,9%.

scholarly journals Sistem Pakar Diagnosa Kerusakan VGA dengan Metode Certainty Factor dan Algoritma K-Nearest Neighbor (K-NN)

2020 ◽  
Vol 4 (2) ◽  
pp. 79
Author(s):  
Rizal Maulana Yusuf Effendi ◽  
Septi Andryana ◽  
Ratih Titi Komala Sari
Keyword(s):  
Machine Learning ◽  
Expert System ◽  
Visual Processing ◽  
Nearest Neighbor ◽  
Diagnostic System ◽  
K Nearest Neighbor ◽  
Certainty Factor ◽  
Nearest Neighbor Algorithm ◽  
Factor Method ◽  
K Nearest Neighbor Algorithm

VGA (Video Graphics Array) is a Video adapter which is very useful for improving the performance and quality of the visual process on a computer, but sometimes there is often a malfunction that cannot be identified the type of damage. The problem is the lack of media to identify the damage that occurs during visual processing. Therefore, the authors created an expert system that can diagnose the type of damage to VGA using the Certainty Factor method as a calculation, using UML modeling as the work process flow of the system on the website, and also equipped with the KNN (K-Nearest Neighbor) algorithm as machine learning. so that it can build an expert system with the PHP programming language MySQL database. The method used in testing is the black box method in testing the system used. The results that can be concluded from this study are; 1) The diagnostic system for detecting damage to the VGA uses the K-Nearest Neighbor Algorithm as machine learning and the Certainty Factor Method as a calculation medium in determining the distance from the type of damage and has suggestions for further actions to deal with and prevent the damage from occurring and also has other possible damage things that are similar to the damage suffered can be accessed quickly and easily to understand, in making scientific research carried out sequentially to facilitate the process, and 2) In addition to diagnosing, there are several additional menus that can be accessed such as the Prediction menu which functions to displays the max and min limits of the temperature of a product, Product Info which functions as a quality product recommendation, and a description that contains a post of details of the damage that can be studied and is expected to help users find solutions to their problems.Keywords:Expert System, PHP, Certainty Factors, Machine learning, K-NN.

Situsparu: Sistem Pakar Untuk Deteksi Penyakit Tuberkulosis Paru

2018 ◽  
Vol 10 (1) ◽  
pp. 41-47
Author(s):  
Ricky Surya ◽  
Dennis Gunawan
Keyword(s):  
Expert System ◽  
Tuberculosis Patient ◽  
The Body ◽  
Certainty Factor ◽  
Good Reliability ◽  
Delone And Mclean ◽  
The World ◽  
Index Terms ◽  
Manual Calculation ◽  
Tuberculosis Prevalence

Tuberculosis is an infectious disease caused by mycobacterium tuberculosis. It can affect some parts of the body: lungs, lymph nodes, intestines, kidneys, endometrium, bones, and brain. According to the survey of tuberculosis prevalence conducted by Republic of Indonesia Ministry of Health in 2013-2014, Indonesia was the second country in the world with the most case of tuberculosis. It makes Indonesia become a country with emergency in lungs tuberculosis. An expert system for lungs tuberculosis detection is built to help people detecting the possibility of suffering from lungs tuberculosis. Therefore, it is hoped that the lungs tuberculosis patient can have early treatment. Certainty factor is used to solve the uncertainty problem delivered by the doctor when examining the patient. Thus, certainty factor is an appropriate method to be used in the expert system for detecting certain disease. This method has been correctly implemented, proved by comparing system detection result to manual calculation result. The expert system has 81.25% accuracy, 83.49% success using DeLone and McLean model, and a cronbach alpha of 0.82 which indicates a good reliability based on the indicators used in the questionnaire. Index Terms— Certainty Factor, Disease Detection, Expert System, Pulmonary Tuberculosis, Situsparu

scholarly journals Skin Cancer Detection and Classification using KNN Technique

2021 ◽  
Vol 9 (VI) ◽  
pp. 3520-3527
Author(s):  
Apeksha R Swamy
Keyword(s):  
Skin Cancer ◽  
Cancer Detection ◽  
Nearest Neighbor ◽  
Early Stage ◽  
The Body ◽  
K Nearest Neighbor ◽  
Fast Evaluation ◽  
Efficient Treatment ◽  
Medical Computer ◽  
Skin Cancer Detection

Skin cancer is a major health issue worldwide. Skin cancer detection at an early stage is key for an efficient treatment. Lately, it is popular that, deadly form of skin cancer among the other types of skin cancer is melanoma because it's much more likely to spread to other parts of the body if not identified and treated early. The advanced medical computer vision or medical image processing take part in increasingly significant role in clinical detection of different diseases. Such method provides an automatic image analysis device for an accurate and fast evaluation of the sore. The steps involved in this project are collecting skin cancer images from PH2 database, preprocessing, segmentation using thresholding, feature extraction and then classification using K-Nearest Neighbor technique (KNN). The results show that the achieved classification accuracy is 92.7%, Sensitivity 100% and 84.44% Specificity.

scholarly journals Identification of Freshness of Marine Fish Based on Image of Hue Saturation Value and Morphology

Jurnal INFORM ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 40-48
Author(s):  
Ekojono Ekojono ◽  
Al Wegi Herman ◽  
Mentari Mustika
Keyword(s):  
Nearest Neighbor ◽  
The Body ◽  
K Nearest Neighbor ◽  
The People ◽  
Image Conversion ◽  
Object Part ◽  
A Cell ◽  
Protein Amino Acids ◽  
Image Object ◽  
Rgb Image

Euthynus is one of the fish that is widely consumed for the enjoyment of the people of Indonesia or abroad, because of its very soft quality, easy to obtain, and contains a lot of essential protein amino acids that are good for the body. This research aims to identify the freshness of the fish purchased based on the eyes and fish gills. The initial process of identifying the freshness of fish uses several methods. Image input process through image object taking using a cell phone camera. The image object is used to determine the value of the RGB image object. RGB color extraction clarifies the value obtained from the image object before proceeding to the next process. Image resize is the process of cutting the image on the desired object part. Image conversion using the HSV method was used to determine the freshness of fish in the gills. The Local Binary Pattern method is used to determine the freshness of the fisheye. The next step is to refine the RGB image into Morphology. The KNN (K-Nearest Neighbor Method) method is used to group objects based on learning data closest to the object. The journal analysis results on the comparison of methods, after 45 trials for each method, found that the Hue Saturation Value method obtained the highest success by 90% and for the texture value obtained 85% success.

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 Expert system for diagnosing diphtheria with k-nearest neighbor method

2018 ◽  
Vol 1 (2) ◽  
pp. 45
Author(s):  
Chavid Syukri Fatoni ◽  
Ema Utami ◽  
Ferry Wahyu Wibowo
Keyword(s):  
Infectious Disease ◽  
Acute Kidney Injury ◽  
Expert System ◽  
Nearest Neighbor ◽  
Kidney Injury ◽  
Initial Diagnosis ◽  
K Nearest Neighbor ◽  
The Common ◽  
Special Concern ◽  
The Government

The Diphtheria cases have special concern by the Indonesian government and are recorded as an extraordinary case (KLB) in 2017. Diphtheria is an infectious disease and cause complications of dangerous and deadly diseases if have not any treated immediately. Along this time, the communities often underestimate the common symptoms of diseases, such as throat pain, flu, and fever. The similarity of Diphtheria symptoms with common diseases and complications such as myocarditis, obstruction on breath, Acute Kidney Injury (AKI), making Diphtheria are rather difficult to treat due to the infections spread quickly. Some complications of diphtheria can cause a death if have not treated immediately and there must be any identification early for diphtheria. Then, an expert system is needed to help the community and the government in diagnosing the diphtheria. An expert system is an information system containing knowledge from experts in order provide information to be used for consultation. The knowledge from experts in this particular system is used as a basis by the Expert System to answer the questions (consultation). The study used the K-Nearest Neighbor (KNN) method, which the method calculates the similarity value of Diphtheria disease symptom. As the result, it can provide an initial diagnosis for Diphtheria before complications occur. The output of this study is the diagnosis of diphtheria based on the symptoms with the accuracy results of 93.056%, as well as providing an initial diagnosis in order to have immediately treating the diphtheria. 

scholarly journals Classification of Hadith Topic of Indonesian Translation Using K-Nearest Neighbor and Chi-Square

2021 ◽  
Vol 7 (2) ◽  
pp. 11-22
Author(s):  
Ghinaa Zain Nabiilah ◽  
Said Al Faraby ◽  
Mahendra Dwifebri Purbolaksono
Keyword(s):  
Feature Selection ◽  
Everyday Life ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Way Of Life ◽  
Chi Square ◽  
The Law ◽  
Test Scenarios

Hadith is the main way of life for Muslims besides the Qur'an whose can be applied in everyday life. Hadith also contains all the words or deeds of the Prophet Muhammad which are used as a source of the law of Islam. Therefore, many readers, especially Muslims, are interested in studying hadith. However, the large number of hadiths makes it difficult for readers or those who are still unfamiliar with Islam to read them. Therefore, we conducted a study to classify hadith textually based on the type of teaching, so that readers can get an overview or other reference in reading and searching for hadith based on the type of teaching more easily. This study uses KNN and chi-square methods as feature selection. We also carried out several test scenarios, including implementing stopword removal modifications in preprocessing and experimenting with selecting k values ​​for KNN to determine the best performance. The best performance was obtained by using the value of k = 7 on KNN without implementing chi-square and with stopword removal modification with a hammer loss value of 0.1042 or about 89.58% of the data correctly classified.

scholarly journals Aplikasi Diagnosa Granulomatous Dermatis Menggunakan Certainty Factor

2019 ◽  
Vol 3 (2) ◽  
pp. 78
Author(s):  
Puji Sari Ramadhan
Keyword(s):  
Artificial Intelligence ◽  
Factor Analysis ◽  
Expert System ◽  
Mycobacterium Leprae ◽  
Final Diagnosis ◽  
The Body ◽  
Limited Information ◽  
Certainty Factor ◽  
Granulomatous Dermatitis ◽  
Reference Tool

Granulomatous dermatitis is a type of inflammatory disease in the inner layer of the skin that causes damage to the nerves, skin layers and motor members of the body. This disease originated in the attack of an aerobic type of mycobacterium leprae which can spread and transmit infection by contact and air, besides this disease is estimated to enter Indonesia in the early V century. At present the diagnosis and treatment of Granulomatous Dermatis is experiencing difficulties, this based on the limited information and lack of knowledge of the community about Granulomatous Dermatis so that later it will result in late or unpreparedness of treatment in patients with Granulomatous Dermatis cases. On the basis of these events, it is very necessary to build a system by acquiring scientific concepts of artificial intelligence that are capable of producing an Expert System which can later be used to diagnose Granulomatous Dermatis by applying the Certainty Factor analysis. With the application of this diagnosis later can help the community and medical experts in diagnosing Granulomatous Dermatis as a reference tool in concluding the final diagnosis.

scholarly journals Application of K-Nearest Neighbor Algorithm on Classification of Disk Hernia and Spondylolisthesis in Vertebral Column

2019 ◽  
Vol 2 (1) ◽  
pp. 57 ◽  
Author(s):  
Irma Handayani
Keyword(s):  
Vertebral Column ◽  
Nearest Neighbor ◽  
Average Length ◽  
Data Classification ◽  
The Body ◽  
Training Data ◽  
K Nearest Neighbor ◽  
Sample Data ◽  
K Nearest Neighbor Algorithm

Vertebral column as a part of backbone has important role in human body. Trauma in vertebral column can affect spinal cord capability to send and receive messages from brain to the body system that controls sensory and motoric movement. Disk hernia and spondylolisthesis are examples of pathologies on the vertebral column. Research about pathology or damage bones and joints of skeletal system classification is rare whereas the classification system can be used by radiologists as a second opinion so that can improve productivity and diagnosis consistency of the radiologists. This research used dataset Vertebral Column that has three classes (Disk Hernia, Spondylolisthesis and Normal) and instances in UCI Machine Learning. This research applied the K-NN algorithm for classification of disk hernia and spondylolisthesis in vertebral column. The data were then classified into two different but related classification tasks: “normal” and “abnormal”. K-NN algorithm adopts the approach of data classification by optimizing sample data that can be used as a reference for training data to produce vertebral column data classification based on the learning process. The results showed that the accuracy of K-NN classifier was 83%. The average length of time needed to classify the K-NN classifier was 0.000212303 seconds.

scholarly journals Comparative Study of Decision Tree, K-Nearest Neighbor, and Modified K-Nearest Neighbor on Jatropha Curcas Plant Disease Identification

2020 ◽  
pp. 55-60
Author(s):  
Triando Hamonangan Saragih ◽  
Diny Melsye Nurul Fajri ◽  
Alfita Rakhmandasari
Keyword(s):  
Expert System ◽  
Decision Tree ◽  
Comparative Study ◽  
Jatropha Curcas ◽  
Plant Disease ◽  
Nearest Neighbor ◽  
Classification Methods ◽  
K Nearest Neighbor ◽  
Disease Identification ◽  
Accuracy Result

Jatropha Curcas is a very useful plant that can be used as a bio fuel for diesel engines replacing the coal. In Indonesia, there are few plantation that plant Jatropha Curcas. But there is so limited farmers that understand in detail about the disease of Jatropha Curcas and it may cause a big loss during harvesting when the disease occured with no further action. An expert system can help the farmers to identify the lant diseases of Jatropha Curcas. The objective of this research is to compare several identification and classification methods, such as Decision Tree, K-Nearest Neighbor and its modification. The comparison is based on the accuracy. Modified K-Nearest Neighbor method given the best accuracy result that is 67.74%.

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