Implementation of a Rabbit Disease Diagnosis Expert System using the Forward Chaining and Certainty Factor Method

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.

Tuberculosis (TB) is an infectious disease that can affect people of all ages, including children, adolescents, and the elderly, and can cause illness and death in over one million people. The disease is spread through coughs or sneezes by people with pulmonary TB, through contaminated saliva, and inhalation by healthy people with weakened immune systems. Therefore, this study aims to develop an expert system to assist in the diagnosis of pulmonary tuberculosis using the Forward Chaining and Certainty Factor methods. This process begins by identifying symptoms reported by the user and then searching for rules in the knowledge base that match those symptoms. This method allows the system to follow a logical flow of reasoning similar to the way a doctor diagnoses a disease. This study used data from 100 patients from 2023 at the Pariaman Community Health Center. Using the Forward Chaining and Certainty Factor methods, three patient data sets with three types of tuberculosis were tested. The percentage results for each type of disease were 100% positive for pulmonary tuberculosis, 0.91% negative for pulmonary tuberculosis, and 0.92% latent for pulmonary tuberculosis, with a confidence level of Very Confident. This research contributes to increasing knowledge and understanding in the field of expert systems, particularly in the application of the Forward Chaining and Certainty Factor methods for diagnosing tuberculosis.

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.

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.

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.

Generally, new prospective students often experience confusion in choosing a major in Computer Science, Informatics Engineering Program at Universitas Al Washliyah Labuhanbatu that truly matches their academic abilities and interests. Often, the decision to choose a major is influenced by the environment, such as following close friends or advice from parents, without considering the students' true potential and talents. However, choosing an inappropriate major can negatively impact their academic future and career. Therefore, it is crucial for prospective students to recognize and understand their academic abilities as well as their special interests and talents. The expert system for major determination developed using the Certainty Factor method is expected to provide an effective solution by combining various indicators of interests, talents, and academic abilities of prospective students. This method utilizes established rules to calculate the certainty level (CF value) for each possible major based on the data and characteristics possessed by the student. By combining CF values from various facts, the system can provide recommendations for the major that best fits the student's greatest potential. This model does not rely solely on academic scores but also considers special interests such as social, creative, and artistic talents possessed by students, making the major selection decision more precise and directed. Thus, this Decision Support System can assist the Faculty of Computer Science, Informatics Engineering Program at Universitas Al Washliyah Labuhanbatu in selecting truly potential new students and providing appropriate recommendations, thereby minimizing the risk of wrong major choices and increasing the likelihood of academic and career success for prospective students.

Indonesia is in the area of equatorial latitude which is known as a tropical climate country. On the other hand, many Indonesian people are prone to suffering typical tropical disease such as typhoid, dengue and malaria. With the motivation to curb disease, they should be informed about awareness, treatment and knowledge regarding to tropical diseases such as the symptoms, causes and early prevention. Web-based expert system is one well-known solution, which can access online via internet. Usually, traditional inference engine is possible to make misdiagnosis in medical domain. In addition, modern inference e.g. Bayes theory are complicated plus insufficient to substitute complete human brain reasoning activities. Therefore, this study aims to hybridize Forward Chaining and Certainty Factor method for diagnosing common tropical diseases suffered by Indonesian people. We have used ten types of tropical disease namely typhoid, dengue, chingkungunya fever, malaria, chicken pox, tuberculosis, diphtheria, pertussis, SARS and elephantiasis along with 38 symptoms for representing every disease into a knowledge base. We compare the results between expert's diagnosis and our proposed web-based expert system after running ten times consecutively. We can conclude that hybridization of Forward Chaining and Certainty Factor methods during developing the web-based expert system, can significantly diagnose tropical diseases properly.

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.

The use of herbal medicine as an alternative treatment is increasingly popular due to its natural benefits and cultural significance. However, a lack of public knowledge about the effectiveness, appropriate dosages, and processing methods of herbal remedies poses a significant barrier to their proper utilization. This knowledge gap often leads to suboptimal or even unsafe usage of herbal medicines. To address this issue, this study proposes an application-based system combining the Forward Chaining and Certainty Factor methods to provide personalized recommendations for the best herbal remedies supporting self-health management. The research aims to enhance accessibility to reliable information on herbal treatments while ensuring accurate and user-specific recommendations. By utilizing the ForwardChaining and Certainty Factor method, this system identifies suitable herbal plants based on the type of disease, processing techniques, recommended dosages, and duration of treatment. Meanwhile, the Certainty Factor method calculates the level of certainty for each recommendation provided. The study’s results showed a validation rate of 90%, indicating that the combination of these two methods effectively bridges the gap between traditional herbal knowledge and modern health needs. This study concludes that the system offers a practical tool for individuals to select and use herbal treatments safely and effectively, promoting better health outcomes.

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.

The objectives of this research are; 1) Creating Android software or applications using the Certainty Factor method, and 2) Early diagnosis of a person's indications using the Certainty Factor (CF) method. The research methodology uses interviews, collects information obtained, the next stage is used with the Certainty Method used to calculate the probability value of MD, MB and CF, researchers to display the diagnosis of Lupus, testing techniques using Acceptance Testing techniques. The results of the study have succeeded in making an application using the JAVA programming language assisted by the Android SDK Platform. The results of the disease obtained a confidence value indication of Dry Eyes, Shortness of Breath due to Lung Inflammation, Continuous Chest Pain for Lupus is 0.968815.

Glaucoma is an eye disease that attacks nerves of human eye and it’s often not realized by sufferer until further damage to the eye nerve occurs. Glaucoma is second most common cause of permanent blindness according to Indonesian Ministry of Health. Permanent blindness caused by glaucoma can be avoided by early detection of disease and appropriate regular treatment. Early detection needed in an effort to prevent glaucoma can be done using an expert system. Expert system is an artificial intelligence program that can make reasoning and thinking like an expert in a particular field. Expert systems with certainty factor methods can describe level of certainty of expert expressions such as possible, most likely, and almost certain. Thus everyone can use an expert system to do glaucoma examination. In this research, diagnosis results will be obtained in the form of a percentage of a person’s CF value for glaucoma which is calculated using the certainty factor method based on the CF value of the symptoms entered by the user. Based on results of tests conducted, there are 8 out of 9 case data that are in accordance with the results of expert system diagnosis using certainty factor method that has been built. In other words, expert system has 90% accuracy in diagnosing glaucoma.

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.