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The reliability of sterilization equipment, such as autoclaves, is essential to ensure patient safety, infection control, and operational continuity in healthcare facilities. Damage or malfunction of autoclaves may disrupt sterilization processes and pose significant risks to medical services. This study aims to develop an expert system for autoclave damage detection using the fuzzy logic method to support faster and more accurate diagnostic decision-making. The proposed system applies fuzzy inference to evaluate the level of damage based on input symptoms provided by users. By handling uncertainty and varying symptom intensities, the fuzzy logic approach enables proportional assessment rather than rigid rule-based classification. The system was designed through knowledge acquisition from technical experts and implemented using fuzzy membership functions and inference rules to determine damage severity levels. Experimental testing was conducted to evaluate system performance and diagnostic accuracy. The results indicate that the expert system successfully generated diagnosis outputs for all tested scenarios, achieving functional diagnostic accuracy within the defined test cases. The system was also able to calculate a quantified damage severity value of 11.6235981% based on the given symptoms, demonstrating its capability to assess damage levels numerically and objectively. Furthermore, the developed system significantly reduces the time required for damage detection compared to manual diagnostic procedures. Automating the evaluation process, it assists electromedical technicians in identifying faults more efficiently and taking preventive or corrective actions promptly. Overall, the implementation of a fuzzy logic-based expert system provides an effective, accurate, and practical solution for improving autoclave maintenance management and supporting healthcare service reliability.

The integration of IT Governance and expert system design offers transformative benefits for enhancing library user services. This research employs the COBIT 5.0 framework to align IT strategies with library objectives while developing an expert system tailored for personalized recommendations. The findings indicate that the expert system significantly improves operational efficiency, service accuracy, and user satisfaction by using user profiles to recommend relevant materials and streamline the borrowing process. Testing revealed high user satisfaction levels, with 96.6% finding the system effective and 100% confirming its efficiency. Additionally, IT Governance ensures strategic integration between technological infrastructure and service quality objectives, enabling data-driven decision-making. The study also highlights challenges, such as the need for robust data management and user training, suggesting areas for future improvement. Recommendations include incorporating machine learning to enhance system intelligence, conducting regular evaluations to maintain system relevance, and testing the scalability of this approach across various types of libraries. By integrating IT Governance with an expert system, this research sets a strong foundation for modernizing library services to better meet user expectations in the digital era.

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.

Collaboration of computer science disciplines with other disciplines has been widely carried out, for example, medical science. Expert systems for disease diagnosis are one of the many computer programs used by doctors to assist them in their work and provide good results. Researchers in the field of artificial intelligence are working to improve existing systems to cover their shortcomings. Expert systems are used in many applications for disease diagnosis. Expert systems have been used in various industries and have had a significant impact. An innovative method for diagnosing diseases is the application of case-based reasoning systems, compared to expert systems. The solution to diagnosing and treating diseases can be found in the expert system for diagnosing pulmonary tuberculosis. Because this web-based system is based on a web application, all pulmonary tuberculosis patients can access it. A rule-based system that applies the CBR method can identify various types of diseases through the use of weighting techniques and offer treatment recommendations.

Expert systems are one type of computer technology that is being used by a used in the medical field to assist physicians in patient examinations is expert systems.The goal of this is to improve patient care both now and in the future.system with expertise  is an application that replicates how an expert would reason to solve a particular problem or acts similarly to an expert due to its understanding of a knowledge base that has to be processed and its ability to solve problems. An expert system's diagnosis of epilepsy leads to the creation of a system that can offer individuals with the condition a consultation service for the purpose of diagnosing the condition and providing information on treatment options. This is demonstrated by the development of numerous technologies that facilitate the work of numerous parties. One of them is computer-related and uses Expert System Science to assist in the diagnosis of epilepsy. The Certainty Factor approach is employed in this study. Thirteen symptoms and three different forms of epilepsy—general, partial, and secondary—were used in this investigation. The study's findings indicate that, based on the chosen symptoms, the most accurate diagnosis is Partial Primary, Partial Secondary, with a confidence level of 74%, and the most accurate diagnosis is Generalized Epilepsy, with a confidence level of 99%.    

The objective of Waikabubak Regional Hospital's information system deployment is to streamline the administrative procedure. Information system performance must be measured or audited to ascertain its maturity in order to give information technology a positive role and to ensure that it operates in line with the hospital's planning and business objectives. Information system audits conducted with the COBIT 4.1 Framework are highly beneficial to management, auditors, and users alike. Determining the information system's maturity level at Waikabubak Regional Hospital is the goal of this study.  The ability to offer suggestions that can be used as raw material for future information system improvements is another goal of this research. The study's findings display the system maturity level score.  From a total of 20 sudomains, the information gathered at Waikabubak Regional Hospital is 3 or at level Defined Process; only 6 of these indicated a score of level 2 maturity level (Repeatable but Intuitive), which is provided as a recommendation.. Keywords: Expert System, Epilepsy, Certainty Factor    

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.

Thyroid gland disease is a disorder that affects the thyroid gland, which plays a vital role in regulating the body's metabolism. Common symptoms associated with thyroid disease include tremors, concentration difficulties, changes in the menstrual cycle, and neck enlargement. At RSUD Dr. RM. Djoelham, Binjai, many patients struggle to understand and diagnose this disease early due to a lack of information and specific symptoms. To address this issue, an information technology-based system is needed to help the public recognize thyroid disease symptoms and provide an early diagnosis. One effective approach for designing such a system is using Case Based Reasoning (CBR), a method based on experience that solves problems by finding similar cases from existing data. This system can process symptoms entered by users, such as dry skin, anxiety, neck enlargement, and others. Based on previous cases, the system will calculate the percentage probability of the disease, thereby providing a more accurate early diagnosis. For example, if the selected symptoms are dry skin, neck enlargement, and shortness of breath, the system can give a 42% probability of a thyroid gland disorder.

Hyperthermia is a condition characterized by symptoms such as dehydration, muscle spasms, dizziness, weakness, nausea, vomiting, and fatigue, which can harm the patient's condition. The causes of hyperthermia can vary, ranging from lack of fluids to excessive physical activity. RSU Putri Bidadari has doctors who are experts in treating various diseases, including hyperthermia. However, several obstacles often occur in the direct consultation process, such as long queues, long distances, limited time, and costs. Therefore, a technology-based system is needed that is able to manage hyperthermia symptom data and help diagnose the disease early, so that patients can get information and early treatment quickly. This method is used to manage the symptoms selected by the patient to determine the possibility of the disease with a high level of confidence. Based on the analysis of the selected symptoms, this system is able to produce the most accurate diagnosis with the case of hyperthermia type Heat exhaustion, with a confidence level of 50.26%.

Tomatoes are plants that were first discovered in South America, closely related to eggplant, potatoes and peppers. Tomato is a fruit that has an attractive red color and is rich in vitamins such as vitamin C. So it is not wrong if tomatoes are very useful for maintaining the body's immune system. Each 100 grams of tomatoes contains 20 calories of calories, 1 gram of protein, 0.3 grams of fat, 4.2 grams of carbohydrates, 5 milligrams of calcium, carotene (vitamin A) 1500 SI, thiamin (vitamin B) 60 micrograms, ascorbic acid (vitamin C). ) 40 milligrams, phosphorus 27 milligrams, iron 0.5 milligrams, potassium 360 milligrams. Tomatoes are also vegetables or ingredients for cooking that are sought after by people to meet their daily needs. This makes the supply of tomatoes from farmers is always in shortage. The lack of supply of tomatoes in the market is caused by a decrease in tomato production or yields. This decrease in production was caused by several obstacles, one of the obstacles that caused crop failure was due to disease. Disease attacks on tomato plants can occur from planting to harvest. Diseases that often attack penicillin plants are sptoria leaf spot, anthracnose fruit bud, fusarium and verticium wilt, brown spot and late blight. Therefore, to handle this, of course, sufficient knowledge is needed to deal with and deal with pests and diseases in tomato plants appropriately. To overcome this, it is necessary to build a system that can diagnose diseases in tomato plants. So that farmers are able to overcome and deal with pests and diseases on tomato plants appropriately. Researchers have done a lot of research by building an expert system to diagnose a disease. With the results of research, an expert system is designed to assist farmers and agricultural extension workers in detecting diseases in soybean and rice plants. From the results of tests that have been carried out using an expert system, 14 different cases in the field are then cross-checked with the results of expert analysis and have a suitability of 93%.

The increasing complexity of public demand for telecommunication services, particularly internet services, has pushed PT. Telkom, as one of the state-owned enterprises (SOEs), to continuously enhance the quality of its services. One of its flagship products, Indihome, offers faster internet connectivity compared to dial-up services. However, Indihome has been frequently criticized by customers due to service disruptions. This indicates a need for developing effective strategies to address customer complaints. The primary issue faced by the public is the lack of knowledge regarding service disruptions, leading to difficulties in explaining the problem to technicians for repair. This research aims to develop a Telkom service disruption diagnosis system that can assist the public in identifying issues early without direct consultation with an expert. The system is developed using an expert system method, where information about service disruptions is processed to generate accurate diagnoses. With this system, customers can identify the type of disruption and provide clearer information to Telkom technicians. The research findings indicate that the most common disruptions are caused by faulty adapters or modems and disconnected configurations, with a density value of 54.49%. This system is expected to improve Telkom’s public service quality, minimize customer complaints, and expedite the repair process for Indihome services.  

Epilepsy, or apoplexy, is a chronic disease characterized by recurrent seizures and impaired consciousness due to disorders of the central nervous system. In developing countries, including in RSU Putri Bidadari, epilepsy management is often hampered by high consultation costs, resulting in suboptimal quality of treatment and patient recovery. To overcome this challenge, a system is needed that can facilitate the diagnosis and treatment of epilepsy more efficiently. By using this method, RSU Putri Bidadari can improve the precision of epilepsy diagnosis and determine more appropriate treatment steps, despite limited resources. The Bayes method, as a statistical approach, offers a potential solution to improve the accuracy of diagnosis through data-based probability estimation of diseases and symptoms reported by patients such as frequent hunger, thirst, urination, weight loss, vaginal infections, easy fatigue, tingling legs, and blurred vision. The analysis results of the system show an estimated probability of 73% for patients suffering from generalized epilepsy. The Bayes method-based system is expected to help RSU Putri Bidadari in providing more effective treatment and improving the overall quality of life of epilepsy patients.

Obsessive-Compulsive Disorder (OCD) is a psychiatric disorder characterized by uncontrollable obsessive thoughts and compulsive behaviors. The disorder triggers anxiety in sufferers that often drives them to avoid situations or places that can trigger obsessions, such as shaking hands or using public restrooms. Proper treatment is necessary to prevent further impact on the quality of life of OCD sufferers. However, early diagnosis is often constrained by limited time and access to medical experts. To overcome this, an expert system based on the Certainty Factor method was developed. This system mimics the thought process of a medical expert in diagnosing OCD using symptoms selected by the user. Certainty Factor is used to calculate the certainty level of each diagnosis based on the inputted symptoms. From the analysis, the system is able to provide diagnoses with high accuracy, even reaching 100% for some OCD cases. These results show that expert systems can be an effective tool in detecting OCD early, thus accelerating the process of proper handling and treatment

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    

Mental health is a state of well-being in which a person is aware of his or her abilities, can cope with normal life stresses, can work productively and contribute to his or her community. Mental health encompasses emotional, psychological and social well-being, and affects how a person thinks, feels and acts. It also determines how a person handles stress, relates to others and makes decisions. Prediction methods that can identify the level of mental health of students are important as a preventive measure. One promising method in this regard is the Naïve Bayes Method. This method has the advantage of being able to solve classification problems on complex datasets, such as student mental health data involving many independent variables. An expert system is a system that attempts to adopt human knowledge into computers so that computers can solve problems as is usually done by experts. The purpose of this study was to find out how to predict the level of mental health of students towards the end of school using the Naïve Bayes method. The results of this study are that the prediction of the level of mental health of students towards the end of school using the Naïve Bayes method can be used and the system created works well, without having to consult a doctor or psychologist.

Vulvodynia is a chronic pain condition affecting the vulva that significantly impacts women’s quality of life. Accurate and early diagnosis poses a challenge due to the often-overlapping symptoms with other conditions and the lack of definitive diagnostic tests. This paper proposes the use of expert system methods as a diagnostic tool for vulvodynia in women. The expert system, integrating medical knowledge with inference algorithms, is designed to analyze symptoms, medical history, and test results to provide accurate diagnoses and treatment recommendations. The study involves the development and evaluation of a computer-based expert system prototype that uses clinical data and medical decision-making to enhance the accuracy of vulvodynia diagnosis. Preliminary results indicate that the expert system can improve diagnostic rates and reduce the time required for identifying this condition, offering a potentially valuable tool for medical professionals in clinical practice.  

Tuberculosis (TB) is one of the infectious diseases caused by Mycobacterium tuberculosis bacteria infection in the human lungs. Tuberculosis is a disease that can be transmitted from people with TB through coughing, sneezing, talking, laughing or singing. Lack of public knowledge about TB and lack of funds for health checks make many people late to be treated. Expert systems are technologies developed based on programs, in accordance with human methods and mindsets. This aims to help people who want to check their health, but are hampered by costs, besides saving time if the examination place is far from the residential environment of the community concerned. Expert systems require a method that can help solve existing problems. In this study, the method used is the Case-Based Reasoning (CBR) method, because the main function of this method is to diagnose the disease. The calculation process of the Case-Based Reasoning (CBR) method which looks for the similarity value or proximity of old cases to new cases of a patient.

The application of information technology has been widely used in medicine. This application provides convenience and smoothness in the medical world to detect symptoms of various diseases, especially malaria. Malaria is still included in the endemic diseases suffered by the community in Binjai City, the more malaria patients, of course the more doctors are needed/work to diagnose patients. Artificial intelligence is one solution and helps doctors in supporting decision making for certain diseases. Building a system to diagnose malaria using the Case-Based Reasoning (CBR) method offers various significant advantages. CBR utilizes experience and knowledge from previous cases, allowing the system to provide a more accurate diagnosis based on patterns and symptoms that have occurred in the past.

The lungs as the only pump for the respiratory system are very important organs for the continuation of life. Diagnosing or checking lung symptoms early can help people recognize the possibility that they are suffering from lung disease, so that treatment or care can be done earlier to prevent the severity of the disease. The method used in this study is the Naïve Bayes method. Naive Bayes is a simple probabilistic classifier that calculates a set of probabilities by adding up the frequencies and combinations of values ​​from the given dataset. An expert system is a computer application that can help decision making in more specific fields with methods that have been analyzed in advance by experts or specialists. This study used variables, namely types of lung disease including Pulmonary Tuberculosis (TB), Chronic Obstructive Pulmonary Disease (COPD), Bronchial Asthma and Lung Cancer. The results of this study are that lung disease or types of lungs can be diagnosed using the web-based Naïve Bayes method, and make it easier for sufferers to consult without seeing a doctor by selecting symptoms of lung disease.

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%.