Certainty Factor Method Research Articles

Expert System for Diagnosing Respiratory Diseases in Cats

Cats are the most common pet in Indonesia with a cat ownership rate of 47% (Rakuten Insight, 2021). Cat owners need to know and recognize the signs and symptoms of the diseases that often occur in cats, especially respiratory problems and diseases. Although vaccines in cats can significantly reduce the incidence of respiratory diseases, they do not eliminate infectious disease pathogens. During the COVID-19 pandemic, it was necessary to adjust health consultations that could reduce the transmission of COVID-19, which is the contactless method. In animal health, there is an online consultation through the WhatsApp platform between veterinarians and cat owners. Cat owners manually type every symptom experienced by the cat. However, there are several shortcomings in the online consultation, including that the symptoms described by the cat owner are unclear, so the diagnosis data is lacking and the consultation fee is quite expensive. Based on the problems that have been mentioned, the purpose of this study is to create an expert system for diagnosing respiratory diseases in cats using the Certainty Factor method. The result of this study is the availability of an expert system that can be used to diagnose respiratory diseases in cats.

Comparison Of Dempster Shafer AND Certainty Factor Methods In Expert System For Early Diagnosis Of Stroke Disease

Stroke is one of endangering disease if not treated properly and could lean to death. Most people unwilling to check their health because of high cost, lack of medical service, medical staff of neurologist and their limited working time. Therefore, we need an expert system that can help in early diagnosis of stroke. The Dempster Shafer and Certainty Factor methods are expert systems methods used in many cases to support uncertainty from the expert. The aim of this study is to compare two methods to determine the best method in the expert system for diagnosing stroke, by calculating symptoms so as to produce CF values in the Certainty Factor method and density values in the Dempster Shafer method. The data used in the study to diagnose stroke consisted of data on eighteen disease symptoms and two types of stroke identified. Based on the results of testing on 105 test data, the accuracy value of the expert system for diagnosing stroke using the Dempster Shafer method is 95.2% and the accuracy value of the expert system for diagnosing stroke with the Certainty factor method is 98.1%.

Exploration of the Dempster-Shafer Theory for Diagnosing Hardware Damage: Comparative Study with the Certainty Factor Method

Comparative research is a research method that compares two or more research objects to expand understanding of them. In this context, we compare the Certainty Factor method and the Dempster-Shafer method in diagnosing computer hardware damage. The Certainty Factor method measures an expert's confidence in the solution that will be provided. On the other hand, the Dempster-Shafer method is a useful theory in situations where uncertainty dominates. This research focuses on comparing the effectiveness of the two methods in detecting computer hardware damage. An experimental approach is used to look for causal relationships between variables controlled by the researcher. The research results show that the Certainty Factor method has an average success rate of 14.51%, while the Dempster-Shafer method reaches 84.27% in terms of error difference. From these results, it can be concluded that the Certainty Factor method is more effective in diagnosing computer hardware damage. Keywords: INISTA, journal, MS.Word template

DIAGNOSIS OF CUCUMBER PLANT DISEASES USING CERTAINTY FACTOR AND FORWARD CHAINING METHODS

Cucumber plants spread and can live in tropical climates like Indonesia. The cucumber plant has many benefits and can be a beauty ingredient. Cucumbers, like other plants, can also have disease attacks, which can threaten farmers. This expert system can help farmers discover diseases that attack cucumber plants and how to control them. The certainty Factor is a method used to measure the certainty of facts to describe an expert's confidence in facing a problem. Forward Chaining is an approach method monitored by data starting from information in the form of facts and supported by rules to reach conclusions. Implementing an expert system for diagnosing cucumber diseases using certainty factor and forward chaining methods will make it easier for farmers and the public to cultivate cucumber plants and get good results. Applying the forward chaining method and factor certainty in this expert system can produce an accuracy level of 95.918%.

Application of Forward Chaining Method, Certainty Factor, and Bayes Theorem for Cattle Disease

Indonesia is a country that has many natural resources, especially mammals. The Papua and West Papua regions are large provinces with abundant natural resources and tremendous livestock potential. The availability of natural resources in the form of live cattle provides a great opportunity to develop animal husbandry in West Papua province. This research was conducted to create a new expert system with a knowledge base to solve the problems that occur and be useful for the community, especially cattle breeders. The current problem is the delay and lack of medical personnel in diagnosing cattle diseases, the distance that must be traveled, which is still very difficult to travel, and the lack of understanding of farmers in early handling when implications indicate animals. So, the Certainty Factor Method and Bayes Theorem with Forward-Chaining search are used to handle current problems. From the results of manual calculations, Certainty Factor Forward Chaining search is a method that has an uncertainty value of 99.84% for 3-day fever compared to Bayes Theorem Forward Chaining search with a value of 50% for worms, 50% for 3-day fever and 50% for nail rot, if applied then Certainty Factor Forward Chaining search is the most appropriate. Likewise, updating the knowledge base must be done from time to time. So that in the future, it can be compared with other methods and Android-based to facilitate current breeders.

Hybrid Architecture Model of Genetic Algorithm and Learning Vector Quantization Neural Network for Early Identification of Ear, Nose, and Throat Diseases

Background: In 2020, the World Health Organization (WHO) estimated that 466 million people worldwide are affected by hearing loss, with 34 million of them being children. Indonesia is identified as one of the four Asian countries with a high prevalence of hearing loss, specifically at 4.6%. Previous research was conducted to identify diseases related to the Ear, Nose, and Throat, utilizing the certainty factor method with a test accuracy rate of 46.54%. The novelty of this research lies in the combination of two methods, the use of genetic algorithms for optimization and learning vector quantization to improve the level of accuracy for early identification of Ear, Nose, and Throat diseases. Objective: This research aims to produce a hybrid model between the genetic algorithm and the learning vector quantization neural network to be able to identify Ear, Nose, and Throat diseases with mild symptoms to improve accuracy. Methods: Implementing a 90:10 ratio means that 90% (186 data) of the data from the initial sequence is assigned for training purposes, while the remaining 10% (21 data) is allocated for testing. The procedural stages of genetic algorithm-learning vector quantization are population initialization, crossover, mutation, evaluation, selection elitism, and learning vector quantization training. Results The optimum hybrid genetic algorithm-learning vector quantization model for early identification of Ear, Nose, and Throat diseases was obtained with an accuracy of 82.12%. The parameter values with the population size 10, cr 0.9, mr 0.1, maximum epoch of 5000, error goal of 0.01, and learning rate (alpha) of 0.5. Better accuracy was obtained compared to backpropagation (64%), certainty factor 46.54%), and radial basic function (72%). Conclusion: Experiments in this research, successed identifying models by combining genetic algorithm-learning vector quantization to perform the early identification of Ear, Nose, and Throat diseases. For further research, it's very challenging to develop a model that automatically adapts the bandwidth parameters of the weighting functions during trainin Keywords: Early Identification, Ear-Nose-Throat Diseases, Genetic Algorithm, Learning Vector Quantization

Expert System for Diagnosing Corn Plant Diseases Using the Web-Based Certainty Factor Method

In the process of corn plant growth, it often faces the threat of various diseases that can hinder optimal growth. Dealing with this issue often requires the presence of a plant expert, such as a field agricultural extension officer. However, limitations in the human resources of agricultural extension officers, time constraints, and geographical distances often make it difficult for farmers to get direct assistance from experts. To overcome these constraints, a system capable of addressing issues related to corn plant diseases, similar to the role of an expert, is needed. In this study, the Certainty Factor method is employed, suitable for an expert system that measures the level of uncertainty, and through testing with 28 sample data cases, the accuracy level of this system reaches 90%.

Diagnosis of Diseases of the Nose Using the Certainty Factor Method

The nose is the human respiratory organ and is a very important sense of smell for patients. Nasal polyps and sinus infections are several diseases that can affect the health of the nose. The nasal cavity reduces inflammation and protects the nasal passages. Many people think these symptoms are flu symptoms and do not immediately seek medical help. So many people find out too late that they have a nasal disease. Public understanding of nasal diseases is still lacking, so many people do not know clearly what disease they suffer from. The certainty factor is to measure something certain and uncertain when making a decision.

Metode Certainty Factor Untuk Mengidentifikasi Gizi Buruk Pada Balita

Malnutrition is a condition of malnutrition caused by low consumption of energy and protein in daily food intake. Malnutrition disease has claimed so many lives and what is even more ironic is that most of these victims are babies under five years old (toddlers). The Expert System Application for Identification of Malnutrition in Toddlers using the Certainty Factor method is a knowledge management system that is easy to use and dynamic. This means that experts can add, change, and remove new knowledge or rules without having to start from scratch. System design was carried out using the Waterfall model and modeling language using UML. Meanwhile, the programming language used is PHP using a MySQL database. This expert system was built to identify malnutrition in toddlers. This expert system uses the Forward Chaining inference method and the Certainty Factor method. The results of consultation tests with this system show that the system is able to determine the type of disease along with initial countermeasures and solutions that must be implemented, based on the symptoms previously selected by the user.

Expert System for Diagnosing Malnutrition Using the Certainty Factor Method

Malnutrition in toddlers causes a negative impact on motor nerve development, inhibits behavioral and cognitive development causing a decrease in academic performance and social skills . In addition, malnutrition during infancy can cause long-term risks that focus on later in life, increasing the risk of disease or disability or even death. With advances in information technology today, it is very helpful in predicting or identifying an event, one of which is an expert system that can help an expert in identifying a disease in the world of medicine. Therefore, an expert system is needed that can help doctors and the public find out the type of malnutrition they are suffering from based on the symptoms they are experiencing. The expert system uses the Certainty Factor method in reasoning to obtain diagnostic results from the symptoms shown. This method uses the value of an expert's belief in the symptoms of a disease. The aim of this research is to apply the certainty factor method in identifying malnutrition and providing definitions and suggestions for the disease suffered. The expert system was built using PHP and MySQL database. The results of applying the Certainty Factor method based on the tested data showed that the disease suffered by the patient was Kwarshiorkor with a Certainty Factor level of 0.958528 or 95%. The results of this test show that the certainty factor method expert system is able to identify a disease based on the symptoms experienced

Expert System to Diagnose Stem Border in Sugarcane Plant with Certainty Factor Method

Abstract: Sugarcane (Saccharum Officanarum L) is an annual plantation crop, which has its own characteristics, because it contains sugar in its stems. Sugarcane belongs to the grass family (graminae) like rice, reeds, corn, bamboo and others. One of the problems that exist in sugarcane plantations at PT Perkebunan Nusantara II is stem borer pests. Losses due to borer attacks can be in the form of a decrease in sugarcane weight, yield and quality of the sap obtained. pests on sugarcane plants cause a decrease in sugar production of about 10%. There are three types of stem borer pests found in PT Perkebunan Nusantara II, namely shoot borer pests, striped stem borer pests, and giant stem borer pests. Meanwhile, biological control is by spreading natural predators of pests which will suppress the population of widespread distribution and breeding of pests which are commonly called parasitoids. Parasitoids will become predators of their respective hosts, so appropriate diagnostic therapy for stem borer pests is needed so that the spread of stem borer pests does not become more widespread and the parasitoids to be spread operate effectively. By using the Certainty Factor method, you can find out the types of pests that are attacking sugarcane plants. Based on manual calculations, the dipole yield was 79% on one of the selected pest types based on the symptoms induced in the sugarcane plant.

Diagnosing Bilirubin Disease in Infants Using the Certainty Factor Method

Poor health and an unhealthy lifestyle are vulnerable to making a person more at risk of experiencing certain health problems or diseases. Bilirubin which is related to health is a metabolite in the form of a yellow pigment derived from the breakdown of heme in hemoglobin. Increased Bilirubin in newborns is a common problem that causes a normal or physiological transition during pregnancy. Al Fuadi General Hospital (RSU Al Fuadi) is a hospital located in Binjai that serves general public health. However, the large number of queues of patients waiting for examination results in a lengthy consultation process and treatment that will be received by Bilirubin sufferers. Based on the existing problems, an expert system is needed that can store information from an expert who can help the hospital. The Certainty Factor method is a method that can prove whether a fact is certain or uncertain in the form of metrics that are usually used in expert systems. Based on the results of trials conducted on this system it can be seen that the level belief from results diagnosis to disease i.e. 93% diagnosed with High Bilirubin disease.

Sistem Pendukung Keputusan dalam Menentukan Strategi Manajemen Resiko UMKM Berbasis Web Dengan Metode Certainty Factor

Risk management plays a crucial role in securing the sustainability and growth of Micro, Small, and Medium Enterprises (MSMEs). Amidst the ever-changing complexity of the business environment, an efficient approach is needed to identify, analyze, and address the risks faced by MSMEs. This research aims to investigate the implementation of web-based Decision Support Systems (DSS) using the Certainty Factor method as a risk management strategy for MSMEs. The risk data obtained will be used to build a knowledge base in the form of a decision support system integrated into a web application. The implementation of web-based DSS provides wide accessibility advantages for MSMEs in managing risks. By utilizing the web application, MSME stakeholders can identify relevant risks in their field of business, analyze the impact and probability of each risk, and receive appropriate action recommendations. The results of this research indicate that the implementation of web-based DSS using the Certainty Factor method is effective in supporting MSMEs in risk management. MSMEs that utilize this application can make more informed and timely risk decisions, reducing potential losses. Therefore, it is hoped that this research can make a significant contribution to enhancing the resilience of MSMEs in facing the complexities of the business environment.

Rancang Bangun Expert System Diagnosa Penyakit Mata Manusia Menggunakan Metode Certainty Factor

When the eyes experience interference, the sufferer will be very uncomfortable in doing their daily activities. In an expert system, the Certainty Factor methods are used to determine the percentage of possible eye diseases suffered and provide accurate diagnostic information based on research conducted. The expert system developed to diagnose eye disease has been successfully implemented using knowledge which includes 22 symptoms and 6 types of related diseases. The purpose of this study is to explore the symptoms displayed in the form of questions in order to diagnose the type of disease with web management system-based software. So that later it gives the results of the percentage of diagnosis of eye disease after based on the symptoms suffered. Of course this research can help experts in dealing with existing problems and make it easier for the general public to get information about eye diseases directly consult through the system without having to check with an eye doctor first, can also provide relief for the underprivileged people who want to consult directly with experts only through Web application.

Application of Web-Based Certainty Factor Method To Developo An Employee Job Stress Diagnosis System To Improve Employee Performance

This research aims to find out how to build an expert system for diagnosing work stress in employees using the certainty factor method. The data obtained was obtained in the following ways: Literature study, interviews, and questionnaires The population in this research is all employees. The sample for this research was 100 employees. The system development method used consists of requirements plan, system design, and implementation. Based on the research results, it was found that the expert system for diagnosing employee work stress uses the certainty factor method, which is easy for users to understand in presenting questions and results by employees, and the system can process data on symptoms, causes, and solutions because employees only select symptoms and do not enter them. symptom values that make users less understanding. This expert system has consistent answers. If you try to select the same symptoms, the results will be the same and in accordance with the expert's results.

Expert System for Diagnosing Inflammatory Bowel Disease Using Certainty Factor and Forward Chaining Methods

Identification of inflammatory bowel disease quickly and accurately is motivated by the large number of patients who come with pain in the abdomen and receive minimal treatment because they are considered to be just ordinary abdominal pain. This study aims to identify inflammatory bowel disease which is still considered by some people as a common stomach ache quickly, and precisely and to recommend therapy that can be done as an initial treatment before getting medical action by medical personnel. The method used in this expert system research is a combination of forward chaining and certainty factors. The forward chaining method traces the disease forward starting from a set of facts adjusted to a hypothesis that leads to conclusions, while the certainty factor method is used to confirm a hypothesis by measuring the amount of trust in concluding the process of detecting inflammatory bowel disease. The results of this study are a conclusion from the process of identifying inflammatory bowel disease which begins with selecting the symptoms experienced by the patient so that the diagnosis results appear using forward chaining and certainty factor in the form of a percentage along with therapy that can be given to the patient to reduce pain in the abdomen. A comparison of the diagnosis results using the system and diagnosis by experts, in this case, specialist doctors, shows an accuracy rate of 82,18%, which means that the expert system diagnosis results can be accounted for and follow the expert diagnosis.

Enhancing Early Detection of Gingivitis and Periodontitis: A Web-Based Expert System Utilizing Certainty Factor Analysis

Periodontal diseases, including gingivitis and periodontitis, are prevalent global oral health issues caused by bacterial infections that damage the tissues supporting teeth, potentially leading to tooth loss. Despite their high incidence, many individuals delay seeking treatment until the diseases reach advanced stages, exacerbating complications and systemic health risks. To address this issue, this research proposes an expert system for diagnosing periodontal diseases utilizing the Certainty Factor (CF) method. The CF method, a branch of artificial intelligence, handles uncertainty in medical diagnostics by combining expert knowledge with patient-reported symptoms to estimate the probability of specific diagnoses. Implemented as a web-based application using ASP.NET and C#, the system provides users with real-time diagnostic feedback and treatment recommendations. Tested with patient data from a dental clinic in Thailand, the system demonstrated a 98% accuracy rate in diagnosing gingivitis and periodontitis. This approach not only facilitates early diagnosis but also reduces the burden on healthcare systems, especially in areas with limited dental care access. The findings underscore the effectiveness of integrating AI-driven expert systems in enhancing public health outcomes and improving accessibility to dental care.

Analisis Metode Certainty Factor Pada Sistem Pakar Diagnosa Kerusakan Sepeda Motor

Motorcycles are a predominant mode of transportation in Indonesian society, comprising 84.5% of the total transportation vehicles in 2021 according to national BPS data. Despite providing convenience in mobility, motorcycles are susceptible to disturbances or damages that can hinder normal usage and potentially lead to accidents. Many motorcycle riders lack knowledge or awareness regarding potential issues with their motorcycles. This research aims to analyze the implementation of the certainty factor method in an expert system for identifying motorcycle malfunctions, with a focus on Giska Servis workshop. The certainty factor method serves as a reasoning tool to determine identification outcomes based on identified symptoms. The results of this study are expected to contribute to facilitating motorcycle riders in diagnosing symptoms of malfunctions in their vehicles. The certainty factor method offers a systematic and structured approach to identifying motorcycle issues. Through the implementation of this method, the research attempts to measure the success rate of the expert system in diagnosing malfunctions. Data from the identification results at Giska Servis workshop will be comprehensively analyzed to evaluate the accuracy and effectiveness of the certainty factor method in this context.By highlighting the success of this method, this research is expected to provide valuable insights for the development of expert systems for motorcycle issue identification. The findings of this study can serve as a guide for workshops and motorcycle users to enhance understanding and management of vehicle issues, thereby minimizing the potential for accidents and extending the lifespan of motorcycles.

Implementasi Sistem Pakar Android untuk Deteksi Penyakit Tanaman Coklat dengan Metode Certainty Factor

This research focuses on issues within the agricultural sector, specifically in cocoa farming. The associated problem is the lack of agricultural extension workers, which results in farmers' limited understanding of cocoa plant diseases and their management. As a consequence, disease identification in cocoa plants has traditionally relied on mere estimates, often proving inaccurate and inefficient in taking appropriate actions. The primary objective of this research is to design and develop an Android-based application utilizing the Certainty Factor method for diagnosing diseases in cocoa plants. The application is envisioned to function as an intelligent consultant for providing disease diagnoses in cocoa plants. The research employs the Research and Development methodology, gathering data from both primary and secondary sources through observations and interviews. The application is tested using both whitebox and blackbox methodologies. Whitebox testing results demonstrate that the system can operate effectively, as indicated by calculations of V(G) and Cyclomatic Complexity (CC). Blackbox testing, conducted with sample tests, also indicates that the system functions as expected and efficiently. In conclusion, the system developed in this research can significantly assist users, particularly farmers, in diagnosing diseases in cocoa plants. The expert system for diagnosing cocoa plant diseases can be relied upon and operates effectively.

Breast Cancer Screening Application Based on Android with the Certainty Factor Method

According to Globocan records, in Indonesia in 2020 there were 396.314 new cancer cases. And 234.511 people were declared dead. Women are a group with a high risk of developing cancer. If cancer is detected at an early stage, this can increase the chance of cure to 80-90%. Early detection of cancer can be done using several methods, for example, for breast cancer, the method of checking can be using the SADANIS (Clinical Breast Examination) and SADARI (Self Breast Examination) methods. In this research, a mobile application will be developed that can be used as a guide in carrying out early cancer detection independently. The early detection system uses an Android-based expert system and certainty factor method. The case study in this research is on breast cancer. Based on the results of accuracy testing with expert diagnosis as a reference, an accuracy value of 90% was obtained. The inaccuracy of this expert system is 10% which can be caused by several possibilities, namely the expert's subjectivity in providing confidence values for disease symptoms or the small number of symptoms entered.