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Gastric disease is a common health problem that can disrupt daily activities if not properly treated. To aid the initial diagnosis process, this study developed a web-based expert system capable of diagnosing various types of gastric disease based on the symptoms experienced by the user. This system uses the Dempster-Shafer method to address uncertainty in decision-making by combining a number of pieces of evidence in the form of symptoms to determine the level of confidence in a disease. The system was developed using the PHP programming language and a MySQL database and designed for easy browser access. Testing demonstrated that the system was able to provide fairly accurate diagnostic results that closely approximated the results of consultations with medical professionals. This system is expected to become an initial solution for the public in quickly and independently recognizing symptoms of gastric disease.  

The purpose of this scientific paper is to design and implement a knowledge-based expert system to assist students in determining their major specializations at Vocational High Schools (SMK) more objectively and accurately. A major problem frequently faced by SMK students is the mismatch in department selection caused by a lack of understanding regarding their own potential and the specific criteria of each competency. To address this issue, the system was developed using the Forward Chaining method as the inference engine. This method operates in a data-driven manner, beginning with the collection of facts such as interests, academic abilities, and psychological aspects, which are then matched against a Knowledge Base consisting of IF-THEN logical rules. Based on system testing results, the inference engine is capable of processing the list of facts to generate a specialization diagnosis for SMK students and provide the most relevant departmental recommendations, such as Computer and Network Engineering (TKJ), Accounting, Office Automation and Governance (OTKP), or Marketing. The research results indicate that the implementation of the Forward Chaining method is effective in accelerating the decision-making process and minimizing placement errors. This expert system is expected to serve as a tool for Guidance and Counseling (BK) teachers in providing appropriate career direction for students based on structured empirical data.

This study aims to design and develop an expert system to assist in diagnosing diseases in dairy cattle at Cibugary Farm using the Forward Chaining method. The background of this research lies in the limited knowledge of farmers in identifying early symptoms of diseases, which often leads to delays in medical treatment and negatively affects dairy cattle productivity. To address this issue, an expert system was designed to replicate the reasoning process of a human expert through a knowledge base containing diagnostic rules derived from observable symptoms. The Forward Chaining method was chosen because of its capability to trace facts from known symptoms toward a conclusion regarding the type of disease affecting the cattle. The system was developed by incorporating common disease symptoms, inference rules, and a decision-making mechanism that simulates expert analysis. Testing was carried out on several diagnostic scenarios to evaluate the accuracy and efficiency of the system. The results of the study indicate that the expert system can provide an initial diagnosis quickly and accurately, producing outputs consistent with expert assessments. This functionality assists farmers in making timely decisions regarding appropriate medical interventions, thereby reducing treatment delays and minimizing the risk of disease transmission within the herd. Consequently, the Forward Chaining-based expert system is expected to serve as an innovative solution to improve dairy cattle health management and support sustainable livestock productivity at Cibugary Farm.

Typhoid fever is a significant health issue caused by the Salmonella Typhi bacteria, leading to symptoms such as fever, abdominal pain, diarrhea, muscle pain, and serious complications if not treated promptly. A common challenge faced by society is limited access to medical professionals, especially in remote areas, and delays in recognizing symptoms. To address this problem, this study designs and implements a web-based expert system using the Certainty Factor (CF) method, which helps diagnose typhoid fever quickly and accurately. The Certainty Factor method is used to calculate the certainty level of the symptoms experienced by the patient, providing a diagnosis result in the form of early-stage typhoid, mild typhoid, or severe typhoid. The system was developed using PHP programming language and MySQL database, and tested at RSUD Djoelham Binjai City. The research data was obtained from patients at RSUD Djoelham Binjai with a case study on patient number 22. The processing of symptoms through Certainty Factor calculation showed that the patient is most likely to have severe typhoid with a certainty value of 0.9443 or 94.43%. This result proves that the Certainty Factor method can be used to assist in providing an accurate early diagnosis of typhoid fever with a high degree of accuracy.

An expert system is a computer program designed to imitate the decision-making ability of an expert in a particular field, combining knowledge and rules obtained from experts to diagnose problems, provide advice, or make complex decisions, especially in notebook damage, so this requires the design of an expert system application for diagnosing notebook damage based on a website for users who support the diagnosis process using the certainty factor method, in terms of explaining the certainty factor which has a concept based on symptoms and diagnoses from the weight of an expert's value and the user's value, then calculated using the formula in CF, the final result is the creation of a website-based notebook damage diagnosis expert system.

Hypertension is a common disease in society and can lead to serious complications if not properly managed. This study aims to design a web-based expert system to assist in diagnosing hypertension using the Certainty Factor and Dempster Shafer methods. The Certainty Factor method is used to measure the confidence level of reported symptoms, while the Dempster Shafer method combines information from various sources to generate more accurate decisions. The system is designed with a user-friendly interface to facilitate access for medical personnel and patients. The research results show that the Certainty Factor method achieved an accuracy 97,9%, while the Dempster Shafer method reache 96,4%. The accuracy difference of 1,4% indicates that the Certainty Factor method is more effective in handling the uncertainty of hypertension symptoms than the Dempster Shafer method.

An expert system is a computer program designed to imitate the decision-making ability of an expert in a particular field, combining knowledge and rules obtained from experts to diagnose problems, provide advice, or make complex decisions, especially in dental diseases, so this requires the design of an expert system application for website-based dental disease diagnosis with a UML model for users who support the diagnostic process using the certainty factor method, in terms of the explanation of the certainty factor which has a concept based on symptoms and diagnoses from the weight of an expert's value and the user's value, then calculated with the formula in CF, the final result is the creation of a website-based dental disease diagnosis expert system.

An expert system is a computer program designed to imitate the decision-making ability of an expert in a particular field, especially in printer damage, so this requires an expert system application to diagnose printer damage to users who support it by obtaining diagnostic results using the certainty factor method, in terms of the explanation of the certainty factor which has a concept based on symptoms and diagnoses from the weight of an expert's value and the user's value, then calculated using the formula on the certainty factor, the final result is the creation of an expert system for diagnosing printer damage using the website-based certainty factor method.

Handling natural disaster victims requires a fast, precise, and fair aid distribution process. In this context, expert systems can be utilized as a decision-making tool in determining the type and amount of aid that should be given to victims. This article develops a desktop-based expert system using the Java programming language, which is able to calculate the type of aid based on the condition of the victim, the level of damage, and the number of affected family members. The method used is a rule-based expert system with if-then logic. The results show that this system can assist field officers in accelerating the calculation and distribution of aid.

Anemia is a condition of reduced red blood cells in the body lower than the normal amount, or what is commonly referred to as erythrocytes in the blood circulation or hemoglobin so that it is unable to fulfill its function as an oxygen carrier to all body tissues. An expert system is a system designed to mimic the experience of an expert in answering questions and solving problems. By using an expert system, a non-expert can solve problems and make decisions that would normally be made by an expert. Expert systems are now being developed in various fields, expert systems in the health sector are widely developed to detect various diseases with various methods. The fuzzy mamdani method is a method that is also often known as the MAX-MIN or MAX-PRODUCT method. The fuzzy mamdani method can help users in terms of detecting diseases based on symptoms that arise and lifestyle and provide diagnostic results for further action. Based on the description above, it can be seen that the fuzzy method is one method that has been widely used in software development for the diagnosis of a disease. Modeling this expert system to determine anemia based on the symptoms felt by patients.

Health is important for every human being. Health, education and income of each individual are three important factors that greatly influence the quality of human resources. Anxiety disorders are a significant mental health problem and can affect an individual's quality of life. Early detection of anxiety disorders is important to provide appropriate intervention and prevent the development of more serious conditions. This research aims to develop an expert system that is able to detect anxiety disorders based on symptoms reported by penggunas. This system uses a forward chaining method and a knowledge base compiled from medical literature and consultations with mental health experts. Several stages of system creation include collecting data on symptoms of anxiety disorders, preparing a knowledge base, implementing a forward chaining inference algorithm, and kuatating the system using test data and expert consultation. The expert system developed in this research is able to provide accurate initial information regarding the symptoms of anxiety disorders in adolescents based on the symptoms input by the pengguna. By utilizing a knowledge base and appropriate diagnostic rules, the system can identify key symptoms that indicate the presence of an anxiety disorder.

Indonesian people mostly tend to ignore health, especially dengue fever because the symptoms that arise are similar to common fever, this causes people with common fever symptoms to be reluctant to see a doctor. This study aims to help the public recognize the symptoms of dengue fever early by using the implementation of the fuzzy method in the dengue fever disease diagnosis system that uses specific datasets as a reference. Mamdani's fuzzy method, which is based on linguistic frameworks and fuzzy concepts, allows for the management of knowledge from experts for intuitive decision-making. The test results using the Confusion Matrix, the system showed an accuracy of 92.8%, an average precision of 90.6%, a recall of 95.8%, and an F1-Score of 92.6%, with an effectiveness value of 100%. This study proves that the system is able to provide information about dengue fever, diagnose based on symptoms, and offer solutions for its treatment.

An expert system for diagnosing diseases in tilapia is one solution to help fish farmers detect and treat diseases that attack tilapia. In this study, an expert system was developed using the VCIRS (Voting Classifier for Integrated Rule Set) method to diagnose tilapia diseases based on the symptoms shown. The VCIRS method was chosen because of its ability to combine several classifiers to improve diagnostic accuracy. This system allows users, especially fish farmers, to enter symptoms observed in tilapia and obtain a diagnosis of possible diseases and appropriate treatment recommendations. The evaluation results showed that this system has a good level of accuracy in diagnosing tilapia diseases, by providing fast and accurate results, and making it easier for fish farmers to make decisions related to fish health. This expert system is expected to increase the productivity of tilapia cultivation by reducing the mortality rate of fish due to diseases that are not detected quickly.  

An expert system is a computer program designed to imitate the decision-making abilities of an expert in a particular field, combining knowledge and rules obtained from experts to diagnose problems, provide advice, or make complex decisions, especially in computer LAN network disruptions, so this is needed application of an expert system for troubleshooting computer LAN networks for users which supports the diagnosis process using the certainty factor method, with an explanation of the certainty factor which has a concept based on symptoms and diagnosis from the weight of an expert's value and the user's value, then calculated using the formula in CF, the final result is the creation website-based computer LAN network troubleshooting expert system.

Chilli farming is an important sector in Ende District, but plant disease problems are often an obstacle that hampers productivity. Farmers often have difficulty recognizing the symptoms of disease in chili plants and determining appropriate treatment steps. To overcome this problem, this research aims to design and build a web-based expert system that can help diagnose chili plant diseases using the Forward Chaining method. This expert system was developed by collecting knowledge from agricultural experts and literature related to chili plant diseases, as well as applying the Forward Chaining method for the reasoning process. Users, especially farmers, can enter the symptoms experienced by chili plants into the system, then the system will produce a disease diagnosis and appropriate treatment recommendations based on these symptoms. This research uses a case study at Maju Tani Agriculture in Ende District to ensure that the expert system developed is relevant to local conditions. This system is implemented in the form of a web-based application so that it can be accessed easily by farmers using devices connected to the internet. The test results show that this expert system can provide accurate and efficient diagnoses, as well as assist farmers in making decisions to overcome chili plant disease problems. It is hoped that this system can increase agricultural productivity and reduce the risk of losses due to plant diseases.

This research aims to develop an expert system that can diagnose diseases related to brain tumors using the Case Based Reasoning (CBR) method. The CBR method works by comparing new cases with previous cases that have been stored in the database to provide appropriate diagnoses and treatment recommendations. This system is designed to assist medical personnel in analyzing patient symptoms, thus speeding up the process of identifying the type of brain tumor. In addition, the system is also equipped with a knowledge base obtained from real cases that have been validated by medical experts. Test results show that the diagnostic accuracy of this system reaches a fairly high level, especially in detecting frequently encountered types of brain tumors. Thus, this system has the potential to be an effective tool in the medical diagnosis process, especially in patients who show symptoms of brain tumors.

Autoimmune diseases, which are disorders where the immune system attacks the body’s own tissues, can affect anyone, including children and adults. These diseases often lead to serious tissue damage and physiological disturbances. Al Fuadi Binjai General Hospital, the primary healthcare facility in Binjai City, faces challenges in diagnosing autoimmune diseases in a timely manner due to limitations in time, cost, and distance. Delays in treatment can exacerbate patient conditions and slow recovery processes. The objective of this study is to develop a system that processes symptom and autoimmune disease data using the Dempster-Shafer method, which allows for uncertainty assessment in decision-making. Patient symptom data collected and analyzed using this method aims to determine the likelihood of autoimmune diseases. The developed system demonstrated high diagnostic accuracy, with the most accurate results for lupus with a confidence level of 94.40%. This result indicates that the Dempster-Shafer method can be an effective tool in accelerating the diagnostic process and improving the accuracy of autoimmune disease management at Al Fuadi Binjai General Hospital    

This research presents an expert system using the extreme programming method to determine the level of ability in public speaking. Public Speaking is an important speaking skill in many fields, but it is often difficult for individuals to evaluate their ability. The extreme programming method is used to provide a reliable level of workmanship in the public speaking system. The expert system will provide recommendations in the form of solutions, or strategies that can help individuals improve their public speaking skills. individuals in improving their public speaking skills. The solutions provided will be adjusted to the individual's ability level, so as to provide effective knowledge. The trust method used is the certainty factor method. The results displayed are information about level of public speaking ability, the level of confidence of an expert along with solutions that can be done next by website users.

Nasal hypertrophy is a swelling that occurs in the nasal concha. This condition is caused because the inferior concha has a larger anatomical size when compared to the other concha structures. The process of diagnosing nasal hypertrophy often requires high clinical skills and experience. RSU Putri Bidadari is one of the hospitals that treats Nasal Hypertrophy disease in patients. Nose hypertrophy disease has several symptoms that are felt which are usually caused by several factors such as exposure to certain allergens, chronic sinus infections, or a family history of similar nasal problems, so several diagnostic tests are needed that can confirm the diagnosis, such as nasal endoscopy to see directly the condition inside the nose, medical imaging such as CT scan or MRI to evaluate the structure of the nose in more detail, or allergy tests to identify the causative allergen. From the above problems, patients really need a system that becomes a recommendation in helping provide information about nasal hypertrophy disease that can diagnose early and take further action to prevent nasal hypertrophy disease. By using the certainty factor method, information from the steps above can be systematically analyzed to determine the level of confidence in the diagnosis of nasal hypertrophy. These factors can be assessed based on severity, presence of typical symptoms, correlation with risk factors, and results of physical examination and diagnostic tests. Based on the results of the CF calculation, the highest value is in the type of nasal hypertrophy disease with the type of Septal Deviation disease having a value of 1 or 100%, in the type of Rhinitis disease having a value of 94.24% and in the type of sinusitis disease having a value of 85.60%. From the results obtained, the system identifies that the patient has nasal hypertrophy with Septal Deviation type by 100%.  

Respiratory tract disease is a common condition that can affect anyone regardless of age. Starting from relatively mild symptoms to alarming symptoms. Although some respiratory diseases are not life-threatening, they should not be taken lightly as they can cause serious complications. What often happens is that it is difficult for a patient to see a specialist doctor because of the limited number of respiratory specialists who cannot fully serve patients, so people often have difficulty if they want to consult directly. This triggers the habit of the community to treat complaints on their own with simple drugs bought freely at drugstores or pharmacies without knowing for sure the disease they suffer, as well as the length of waiting for queues, consultation fees that are quite expensive and not everyone has a short distance to the hospital prefer not to go to a specialist. Like other organs of the human body, breathing is also prone to various diseases. Respiratory organs will be disrupted and can even cause death. By using the Naïve Bayes method above, it is known that the diagnosis of respiratory disease is that the young female patient is diagnosed with a type of respiratory disease called Farangitis (P05) with a percentage of 47.44%.