Expert System For Detection Glaucoma Disease Using Certainty Factor Method

An expert system is a system that adopts the expertise of an expert in a particular field into the system. Rabbits are one of the animals that are close to humans and have high body adaptability. However, rabbits are not free from the threat of disease. Treatment requires more costs so we need a way to find out the disease and the solution so that we can take the necessary actions. To overcome this, an expertise media was created that can be accessed by rabbit owners online using the Forward Chaining and Certainty Factor methods. The expert system must be able to work under uncertainty, so the Forward Chaining (FC) and Certainty Factor (CF) methods were added to overcome this problem. Forward Chaining is a decision making method that is commonly used in expert systems and the Certainty factor method is a method to prove the uncertainty of an expert's thinking. The system diagnosis results display possible diseases suffered by rabbits and provide treatment solutions for diseases with the largest CF values. With the calculation results, the level of confidence information based on the table is Definitely Yes. The accuracy of the system output is proven from the results of system validity with experts.

Kidney disease is a prevalent health issue affecting millions of people globally. Early and accurate diagnosis of kidney diseases can help in the timely and effective management of the condition. Expert systems, such as those using the Certainty Factor (CF) method, can provide doctors with valuable assistance in diagnosing kidney diseases more efficiently and accurately. This study aims to develop a kidney disease diagnosis expert system using the CF method. The developed system consists of data collection, data storage, and data processing components, with the CF method used to calculate diagnostic confidence levels and decision-making based on predetermined rules. The knowledge acquisition process was carried out by interviewing three nephrologists to obtain rules for diagnosing kidney diseases. The expert system's evaluation is conducted by comparing the system's diagnostic accuracy with a specialist doctors. The results show that the developed expert system has an accuracy rate of 85.7% in diagnosing kidney diseases. The system also has a user-friendly interface, which allows doctors to input symptoms and obtain a diagnosis quickly and accurately. The developed system has several advantages over traditional diagnosis methods. It can diagnose multiple kidney diseases simultaneously and provide a differential diagnosis, allowing doctors to choose the most appropriate treatment plan for their patients. The system also has the potential to reduce diagnostic errors and improve patient outcomes.

The management of complex neurological diseases such as Multiple Sclerosis (MS) requires accurate and efficient diagnostic approaches. To enhance diagnostic precision, a study has conducted a comparison between two approaches within the framework of an expert system, namely the Case-Based Reasoning (CBR) Method and the Certainty Factor (CF) Method. The primary objective of this study is to evaluate the effectiveness of these two methods in supporting the diagnosis process of Multiple Sclerosis. The Case-Based Reasoning Method is an approach that relies on past experiences to address new issues. Within an expert system, CBR utilizes knowledge from previous cases to identify diagnoses that align with the current situation. On the other hand, the Certainty Factor Method is an approach that measures the confidence level in a statement based on rules and associated confidence factors. This study makes use of a dataset containing information from previous cases related to the diagnosis of Multiple Sclerosis. By employing both of these methods, an expert system is developed to provide diagnostic recommendations based on inputted symptoms and data. The effectiveness of both approaches is evaluated through diagnostic accuracy, computational speed, and confidence levels in the generated results. Research findings indicate that both methods have their respective strengths and weaknesses. The CBR method tends to yield accurate results by referring to similar cases in the past, but it may encounter challenges in unique or rare cases. On the other hand, the Certainty Factor Method has the ability to handle uncertainty and can produce results with measurable confidence levels. However, dependence on predefined rules may limit adaptation to new cases. In conclusion, this study underscores that there is no singular perfect approach within expert systems for diagnosing Multiple Sclerosis. Both the CBR and Certainty Factor methods contribute in their own ways to improving accuracy and confidence in the diagnosis process. Therefore, integrating these two methods could be a promising direction for the development of expert systems in the future.

Covid-19 is a new type of virus that is currently a pandemic in almost all countries. The limitations of experts for diagnosis and recommending rapid action to patients who are diagnosed positively, as well as the limitations of the expert area in the environment wh ere the Covid-19 outbreak occurred. This expert system aims as an early diagnosis system for Covid-19 so that patients can receive treatment immediately, and help stop the wider spread of the virus. Using predetermined parameters and there is also a system that can later make it easier for users to find out the diagnosis of Covid-19 caused by the coronavirus. The method used for this expert system research is Certainty Factor (CF). This method aims to showed how much the confidence value is based on the clinical parameters given by MYCI. The advantage of the Certainty Factor method is that it can be measured something that is certain or uncertain. And to maintain accuracy, Certainty Factor can only process two data. The Expert System uses the Certainty Factor (CF) method to diagnose Covid-19 disease, starting with the collection of symptom data and giving confidence values by the expert on each symptom, then the symptom data input stage from the user and the system will calculate based on the Certainty Factor rule, multiplying the weight value namely user CF and expert CF, combining the results of the multiplication of each symptom, so that a percentage of confidence is obtained as the final diagnosis result. After calculating the accuracy test of the 3 rule system with 99% positive Covid-19 results, 48% most likely Covid-19 and 29% negative Covid-19.

Depression is a growing mental health concern among university students, often fueled by academic pressure, social demands, and personal stress. This study presents the development of an expert system using the Certainty Factor (CF) method to diagnose depression specifically among students at Ngudi Waluyo University. The system categorizes depression into mild, moderate, and severe levels based on 12 validated symptom statements and expert-defined diagnostic rules. Implemented with PHP, JavaScript, and CSS, the system offers a user-friendly, accessible, and anonymous platform for self-assessment. Testing yielded an accuracy rate of up to 79% in diagnosing depression severity and a 71.7% user satisfaction rate based on a User Acceptance Test (UAT) involving 32 students. Results demonstrate that the system can effectively support early detection and mental health awareness within academic environments. Despite some limitations in UI and feedback depth, the expert system shows strong potential for broader application and further enhancement.

Student health directly affects the quality of learning and academic achievement. When sleep patterns and daily routines become irregular, students face a higher probability of developing health-related problems. To address this issue, a web-based expert system was designed and developed to detect student health risks based on sleep patterns and daily activities, applying the Certainty Factor (CF) method, with a case study conducted at STIE BPKP. The Certainty Factor method was used to quantify the degree of confidence in a particular health risk by combining symptom data selected by users with certainty weights assigned by domain experts. The system was built as a web application featuring three core functions: symptom data input, diagnostic processing, and output presentation in the form of health risk levels along with actionable recommendations. Functional testing was carried out to verify that each module operated correctly and that diagnostic outputs aligned with the expected Certainty Factor calculations. Findings from this study confirm that the expert system successfully assists students in assessing their health risk levels in a fast and accessible manner. Beyond individual use, the system also serves as a supporting instrument for academic institutions seeking to strengthen health awareness programs among their student population. As a result, this expert system holds practical value as an early-warning tool for student health risk detection.

Motorcycles are the main means of transportation that is widely used to support the activities of their societies. Prior to the development of technology, motorcycles still use carburetors. But motorcycles are still using carburetor system is still quite wasteful. With the development of technology, there is now applying the Yamaha brand motorcycles capable of supplying petrol injection engine needs. Motorcycles injection technology is much more efficient compared to motorcycle carburetor technology, in addition to economical motorcycle of this type has a performance engine and a reliable power. But frequent obstacles that cause damage to the injection Yamaha motorcycle, disrupting users.An expert system is a system that adopts the expertise of an expert an expert in a particular field into the system or a computer program is presented with a display that can be used by a user who is not an expert so that system users can make a decision or set policy like an expert in finishing problem. Certainty factor method used to obtain the value of the certainty of damage from an expert motorcycle injection, so that the user also gets the value of the damage certainty selected symptoms of the experience of experts. The results of diagnostics in expert system that has been made that if the user mencheklis symptoms such as clogged when rotated gas engine, electric distater difficult / lift, lights up when the engine is difficult to use the kick starter, often emit exhaust sound of an explosion, it can be concluded from the analysis that the damage found on the spark plug with a value of MB = 0.99, MD = 0:22 and Certainty Factor value of 76.66%. The diagnosis ever made by a visitor or mechanical accessible again by entering an email to the database.

Plant disease is one of the reasons that cause the destruction of plant. It affects plant productivity and quality. Most of the farmers made mistake in cope with this problem because of the lack of knowledge. Expert system is a solution that has been widely used for identifying disease. This paper presents an Android-based expert system to help identifying tomato diseases. Data used in this expert system consist of 16 data of tomato diseases, 53 data of symptoms, and 20 variety of rules. This paper implements forward chaining and certainty factor method. Forward chaining is used as a reasoning method to get the result of disease identification. Certainty factor is used as a calculation method to obtain accuracy degree of identification results. Testing has been done through two stages, internal and external. The result from internal testing shows that tomato expert system works properly and fit perfectly in various android devices. External testing is done by giving questionnaire to 44 respondents. The result of questionnaires shows that tomato expert system is categorized as “good” by them.

Lack of blood or anemia is a condition when the body lacks healthy red blood cells or when red blood cells do not function properly. As a result, the organs of the body do not get enough oxygen, making people with anemia pale and tired easily. Anemia is often ignored because it is considered just an ordinary disease. But if left untreated, this disease can be dangerous. So that people are expected to also have knowledge about this disease and its symptoms and how to handle and prevent it so that the risk can be reduced. An expert system is a form of utilizing computer technology that adopts human knowledge to computers, so that computers can solve problems as is usually done by experts. An expert system is a computer system that is able to imitate the reasoning of an expert with expertise in a particular knowledge. An expert system can replace the role of an expert by utilizing a computer which in the end can provide a definite solution as done by an expert. The expert system for diagnosing anemia is built using the certainty factor method which can provide results in the form of the possibility of anemia suffered, the percentage of belief, symptoms, and treatment. The results of this study indicate that the expert system that was built can determine the value of the degree of confidence or the certainty factor of the data obtained from the results of consultations with patients through the certainty factor method.

Lungs are a very importand part of the human organ, which functions as a place for oxygen exchange. This organ that is located under the ribs has a very heavy task, as well as the pollution of the air we breathe everyday which will cause various diseases in the lungs. Lung disease is a disease that is common to everyone, and there are still many who are less concemed with lung healty, so that is causes many indications of lung diseas. Expert system is a system that uses human knowledge recorded in a computer to solve a problem. The purpose og this study was to datermine the accuracy of disease identification in the lungs using the Certainty Factor method. The date obtained is datae about the symptoms that prove wherher a person has lung disease or not and conduct an analysis of the date, so that later conclusions can be abtained from the facts found using an expert system of the Certianty Factor method. The date obtained is date about the sympyoms thet prove whethera person has lung as a problem solving metric which is a parameter value to show the amount of trust. The result of the research from an expert system on pulmonary disease with pulmonary tuberkolosis (TBC) with a certainty level og 68%. Expert system on lung disease using the Certainty Factor method can make it easien for sufferes to know and handle prevention and handling

The medical and technology fields are rapidly advancing, leading many people to use computers to help diagnose, prevent, and treat human diseases. One major issue in the medical world is the imbalance between the number of patients and doctors. Additionally, most people lack medical training, so when experiencing symptoms of a disease, it is often difficult to immediately know the correct steps to take. Eye diseases vary in severity, ranging from mild to severe. One common eye disorder affecting many people is refractive error, which generally falls into two categories: hyperopia (farsightedness) and myopia (nearsightedness). Early detection of symptoms related to refractive errors requires accurate and prompt diagnosis. Therefore, with the rapid development of technology, it is essential to develop systems capable of early detection of eye diseases, especially refractive errors, by using technology that mimics human expert capabilities, such as expert systems. This expert system integrates expert knowledge within two main environments: the development environment and the consultation environment, helping the community diagnose diseases more easily and efficiently. For example, the use of the Certainty Factor method in expert systems enables the calculation of diagnostic certainty levels based on the combination of symptoms reported by patients and expert knowledge, achieving a confidence level of up to 96.7%. This demonstrates that expert system technology can be a valuable tool in addressing the imbalance between patients and doctors while improving access to faster and more accurate diagnoses. To build such systems, Microsoft Visual Studio .NET provides a complete set of tools for developing ASP.NET web applications, XML Web Services, desktop applications, and mobile applications. Within Visual Studio, .NET programming languages such as Visual Basic, Visual C++, Visual C# (CSharp), and Visual J# (JSharp) are used in a unified integrated development environment (IDE), enabling developers to efficiently share tools and resources to create reliable and user-friendly expert system applications. The system was developed and tested using symptom data from 40 patients collected at the Tanjung Sarang Elang Community Health Center. The testing showed a diagnostic accuracy of up to 96.7% in detecting symptoms of both hyperopia and myopia.

Dental and oral health is sometimes not a priority for some people, even though dental and oral diseases have a serious impact on health in general, because teeth and mouth are a place for germs and bacteria to enter, so they are likely to interfere with the health of other body organs. Periodontal disease is a gum disease caused by bacteria that damage the tissue supporting the teeth and cause tooth loss. Periodontal disease is divided into 2, namely gingivitis and periodontitis. An expert system is a system that tries to implement human knowledge on a computer so that computers can solve problems as experts do. The certainty factor method is used for calculations because the certainty factor method can solve the uncertainty of a problem by measuring a person’s belief. The result of this research is an expert system that can produce the name of periodontal disease and the level of certainty for the user’s disease. This application has 100% accuracy based on test results on 20 medical record data, it is obtained that the 20 data match with the results of the system diagnosis.

Craniopharyngioma is a rare benign brain tumor that originates from embryonic tissue of the pituitary gland. It is a type of brain tumor that develops near the pituitary gland at the base of the skull. Its prevalence is approximately 2-4% of all brain tumor cases. Collaboration among the medical team is crucial in providing optimal perioperative care. In diagnosing craniopharyngioma, an expert system can be utilized. The Expert System aids in identifying problems and providing appropriate solutions. Its existence can expedite the process of problem identification and system repair. Additionally, the Expert System can also help reduce consultation costs with experts, as it can provide solutions comparable to those given by experts. This study aims to compare the results of the Dempster-Shafer, Case-Based Reasoning, and Certainty Factor methods in diagnosing craniopharyngioma. The study will determine the percentage results of the Dempster-Shafer, Case-Based Reasoning, and Certainty Factor methods in diagnosing craniopharyngioma to ascertain which method is most accurate in diagnosing the disease. The percentage results from the application of the DS and CF methods yielded the same result of 99.8%, whereas the CBR method only achieved a diagnosis percentage of 51.4% for craniopharyngioma. Hence, it can be observed that the CBR method yields the lowest percentage diagnosis for craniopharyngioma.

Currently, expert systems are widely used in various fields, one of which is the use of expert systems in the field of animal health such as chickens. In the world of chicken farming, every farmer, both small scale and large scale, pays attention to the health of chickens, where health will affect profits. However, many of the community breeders find it quite difficult to get information about livestock problems, such as the process of recognizing quickly and accurately, it is difficult to detect the types of diseases that are caused which are generally similar and the same and there are other difficulties because there are other things so they are reluctant to come to the veterinarian. Based on these constraints, the authors propose an expert system software engineering system to determine chicken disease using certainty factor methods. This system can help in determining a decision and providing more accurate information, because system decisions not only rely on rules, but also generate probability and percentage values using certainty factor methods so that it is easier for small scale breeders and can save time and costs as well as It makes it easier for users to recognize the types of diseases, especially those caused by viruses and bacteria, because the system user only selects the symptoms that appear on the consultation page and after that a decision of the disease and its solution will appear.

This study aims to create an expert system at Rafflesia Hospital of Bengkulu City, based on a website and using the PHP programming language, in diagnosing bronchitis using the certainty factor method, therefore the results of the diagnosis can be first aid when a user or patient suffers from bronchitis. The research method used is the certainty factor method, namely the certainty factor, thus the user or patient can choose. conditions according to the symptoms experienced from bronchitis. The program created is an expert system for diagnosing bronchitis using a website-based certainty factor method so that it can be accessed on laptops, computers, or mobile phones with the condition that it must be connected to the internet. This program can generate a percentage level of the possibility of how many percent of patients suffer from bronchitis, and this program also produces solutions to bronchitis therefore, it can be first aid when the user or patient suffers from bronchitis. Suggestions from the author are for agencies to be able to maintain and always update this expert system program, so then this system can run and be maintained properly.