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Hospitals often face difficulties in determining patient treatment priorities due to limited medical resources and the uncertainty of patient conditions. Conventional prioritization methods tend to rely on subjective judgment, which can lead to inconsistent decisions and delays in treatment. This study aims to apply fuzzy logic in a decision support system to determine patient priority levels more objectively and systematically. The proposed method utilizes a fuzzy inference system that processes several criteria, including the severity of symptoms, vital signs, patient age, and waiting time. These criteria are represented as fuzzy sets and evaluated using a set of inference rules to generate priority classifications. The results indicate that the fuzzy logic–based system is able to classify patient priorities more consistently and transparently compared to manual assessment. The system provides clear priority categories that can support medical staff in making faster and more accurate decisions. The findings imply that the implementation of fuzzy logic in hospital decision support systems can improve the quality of healthcare services, enhance fairness in patient handling, and optimize the allocation of medical resources, particularly in emergency and high-demand situations.

Hirschsprung’s disease is a congenital disorder caused by abnormal nerve cell development in the large intestine, leading to chronic intestinal obstruction in infants. This condition often manifests through symptoms such as constipation, abdominal distension, vomiting, and failure to thrive. The weak immune system of infants makes them highly susceptible to bacterial infections and further complications. At Bidadari General Hospital, there were 110 patients suspected of having Hirschsprung’s disease. One of the major challenges in managing these cases is the limited number of medical specialists, particularly pediatricians and pediatric surgeons, resulting in long waiting times for accurate diagnosis, especially during peak service hours. To address this issue, this study applies the Dempster-Shafer method in an expert system to assist in diagnosing Hirschsprung’s disease based on clinical symptoms. The method effectively handles uncertainty and combines multiple pieces of medical evidence to produce more accurate diagnostic probabilities. The analysis results show that from the selected symptoms, the highest diagnosis probability corresponds to short-segment Hirschsprung’s disease with a confidence level of 71.54%. These findings suggest that the Dempster-Shafer method can serve as an effective alternative tool to support early and accurate diagnosis of Hirschsprung’s disease in infants.

Syndrome is a serious problem in children's health because it has a major impact on growth and development, especially in terms of intelligence and daily activities. Down Syndrome, as one of the most well-known chromosomal disorders, is often the main cause of intellectual developmental disorders, hypotonia, facial dysmorphism, early onset of Alzheimer's disease, and various behavioral disorders. Diagnosing syndrome diseases in children is often difficult due to complex and varied symptoms, requiring lengthy, costly, and time-consuming medical evaluations. This study aims to design a Case-Based Reasoning (CBR)-based expert system for diagnosing syndromes in children, which is expected to help accelerate the disease identification process and provide more effective and efficient solutions. The method used is the development of an expert system with a CBR approach, in which the system performs calculations and matching based on the symptoms selected by the user against the available case base. The results of the study show that from symptom inputs such as wide hands with short fingers, short stature, small head, stunted growth, small lower jaw, abnormal body appearance, and weak joints, the system was able to diagnose Klinefelter syndrome with a percentage of 43.58%. This system can be an alternative for patients or families who have limited time and funds to obtain medical consultations, so that diagnosis and follow-up can be carried out more quickly and efficiently.

Dengue Hemorrhagic Fever (DHF) is an infectious disease caused by the dengue virus and transmitted through the bite of Aedes aegypti mosquitoes. In 2023, 48 cases of DHF were reported in the Kebun Lada Public Health Center area, reflecting a high incidence rate and limited medical resources in managing the cases. This situation emphasizes the need for an alternative solution that can support a fast and accurate diagnostic process. This study aims to develop an expert system for diagnosing DHF using the Case-Based Reasoning (CBR) method. CBR functions by comparing the symptoms experienced by patients with previous cases stored in the knowledge base, thereby producing relevant diagnostic recommendations. The proposed system is implemented as a web-based application using PHP as the programming language and MySQL as the database management system. The expected outcomes of this study are to assist medical personnel in reducing diagnostic time, improving the accuracy of decision-making, and increasing the effectiveness of health services in primary healthcare facilities. In addition, the system is designed to provide wider access for the community to recognize early symptoms of DHF, which can contribute to preventive actions and reduce the risk of severe complications. Thus, the developed expert system has the potential to become a practical solution to overcome the shortage of medical personnel and enhance public health awareness.

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.

Coffee is a leading commodity in Indonesia's agricultural sector, possessing high economic value and providing a livelihood for many farmers. However, coffee plant productivity often declines significantly due to various diseases affecting the leaves, stems, and berries. This situation is exacerbated by the lack of knowledge among most farmers in recognizing early disease symptoms, resulting in delayed treatment. Consequently, crop losses are unavoidable. Based on these challenges, this study aims to design and build an expert system capable of diagnosing coffee plant diseases quickly, precisely, and accurately using the Bayesian Theorem method. This method was chosen because it can calculate the probability of a disease occurring based on observed symptoms in plants. The Bayesian approach allows the system to provide more reliable diagnostic results by updating the probability values ​​as new evidence is introduced. The developed expert system is web-based, making it easily accessible to users, both farmers and other interested parties. Users simply select the symptoms observed in coffee plants, and the system will then provide a diagnostic result in the form of possible diseases and their probability levels. Test results indicate that the system is capable of providing fairly accurate diagnostic results and can be used as a basis for farmers in making initial decisions regarding coffee plant disease management. With this expert system, farmers are expected to improve their ability to detect coffee plant diseases early, thereby maintaining crop productivity. This expert system is expected to be an effective decision support tool for farmers to reduce crop losses and improve agricultural sustainability.

The increasing number of mental health disorders in various countries has created an urgent need for innovation in the diagnosis and treatment process. This problem not only impacts individuals' quality of life but also creates a significant social and economic burden. One solution that is beginning to be widely researched is the use of artificial intelligence (AI) in the field of mental health. This research used a literature review of various previous studies discussing the role, application, and impact of AI. The results of the review indicate that AI technology, particularly in the form of digital applications such as chatbots, has great potential to support the recovery process for patients with mental disorders. AI-based chatbots can provide responsive, two-way interactions, so users feel heard and receive initial emotional support. One technical approach used is Natural Language Processing (NLP), which enables the system to understand natural human language. Simultaneously, Long Short-Term Memory (LSTM) algorithms are used to analyze language patterns and detect symptoms of depression more accurately. Various studies have reported that the application of NLP and LSTM can improve the reliability of diagnoses and provide responses tailored to user needs. Furthermore, AI can provide personalized recommendations, tailor interventions to the user's condition, and monitor mental health developments in real time. This has the potential to assist mental health practitioners in making faster and more informed decisions. However, the adoption of AI among practitioners remains relatively low. Influencing factors include limited technological understanding, limited infrastructure, and debates over ethical aspects and data privacy. Therefore, while AI has significant potential to improve the quality of mental health services, regulations, ethical guidelines, and synergy between technology and healthcare professionals are needed to ensure safe and effective implementation.

Rapid technological advancements have brought convenience to various fields, including healthcare. Osteoarthritis (OA) is a chronic degenerative joint disease that often affects the knees and hips, particularly in the elderly, and is a major cause of pain, joint dysfunction, and reduced quality of life. The prevalence of OA increases with age, with risk factors such as obesity, excessive activity, and muscle weakness. Early and accurate diagnosis is essential for appropriate treatment. This study aims to develop a diagnostic system for inflammatory arthritis, specifically osteoarthritis, using the Dempster-Shafer method. This method was chosen because of its ability to combine various evidence and expert beliefs to produce a more accurate diagnosis. By utilizing mathematical proof theory, this system is expected to assist medical personnel in detecting OA symptoms more efficiently. The research findings are expected to contribute to the healthcare sector, particularly in improving the accuracy of osteoarthritis diagnosis, allowing for earlier and more appropriate treatment. This system can also be a supporting tool for doctors and patients in understanding joint health conditions.

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.

Liver disease is an increasingly prevalent health problem among the productive-age population, primarily driven by unhealthy lifestyles, alcohol consumption, obesity, and the risk of hepatitis infection. Liver dysfunction often presents no specific symptoms in its early stages, making early detection challenging. The Community Service Program (PKM) conducted in Grogol Village aimed to raise public awareness about the importance of early liver function screening through SGOT and SGPT enzyme assessments. This activity included education on risk factors as well as on-site liver enzyme testing for participants. Among the 71 participants, 18 individuals (25.35%) had elevated SGOT levels, and 17 individuals (23.94%) showed SGPT levels exceeding normal limits. These findings highlight the importance of early liver function screening in preventing the progression to chronic liver diseases such as NAFLD or hepatitis, and in serving as an indicator of cardiometabolic risk. Therefore, routine liver enzyme testing can serve as a foundation for increasing public awareness about the importance of maintaining liver and metabolic health in a sustainable manner.

The development of computer technology helps many aspects of life. One aspect of life that takes advantage of technological developments is the health sector, in order to solve problems including brain tumors. Brain Tumor Disease is the growth of abnormal cells in or around the brain in an unnatural and uncontrolled manner. Patients with brain tumors continue to increase every year, because the initial symptoms are often underestimated. Therefore created a software that can help diagnose brain tumors using the certainty factor method

Heart rate is an important indicator of overall heart health. Changes in heart rate can indicate underlying health problems, even before other symptoms appear, especially for the elderly population who are vulnerable to cardiovascular issues such as heart disease, stroke, and arrhythmia. Therefore, a heart rate monitoring device is needed that can monitor heart rate and allow early detection of such cardiovascular disorders. Along with modern technological advancements, heart rate monitoring devices are now available for everyone, but heart signal diagnosis still needs to be conducted by doctors or medical professionals. A heart monitor can be made using a heart rate sensor attached to a NodeMCU device. The Pulse Sensor, which functions to detect human heartbeats, can be placed in three measurement locations: on the finger, hand, or forehead. The data is then processed by NodeMCU, and the measurement results, which are Beats Per Minute (BPM), will be displayed on a website and stored in a database. The test results show that the average heart rate measurement using the device is 74 BPM, while the manual measurement is 74 BPM, with an accuracy of 97.74%, and it takes 60 seconds to display the average Beats Per Minute (BPM).

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.

E-commerce is all activities selling goods and services carried out through electronic media. Shrimp Cracker Shop, is a business that operates in the field of selling typical souvenirs. Currently, sales of goods are still carried out manually, namely by buyers coming directly to the shop or by purchasing by telephone. This type of research uses a descriptive method with a qualitative approach. This research provides an accurate description and explanation of the conditions or symptoms faced. The materials used in this research are data obtained from the results of interviews and later the results of the interviews can find out what needs are needed to build a website. System Design Methods in Designing a The system definitely requires modeling so that the results of the system design that you want to achieve can obtain maximum results. What the author uses is Unified Modeling Langue (UML). Which consists of use case diagrams, class diagrams, and activity diagrams. In this research the author uses the Rapid Application Development system development method. The software development method in this research uses the Rapid Application Development (RAD) model system development method. After the design is carried out, the researcher draws conclusions namely the e-commerce design of the Manda Shrimp Cracker Shop was successfully created to help promotion and sales. The results of testing the Blackbox Testing software system were declared successful, and the feasibility test using a Likert scale resulted in a score of 93% (very feasible). Overall, the results were successful and in accordance with what was designed.

Osteoarthritis is a joint disorder that develops degeneratively due to chronic inflammation in the joint. Symptoms involve the gradual breakdown of cartilage, osteophyte formation, and changes in the synovial membrane. Osteoarthritis sufferers often experience a decrease in daily activities. In particular, Knee Osteoarthritis is the most common type of Osteoarthritis that causes joint pain and limitation of motion in the knee, characterized by damage to the joint cartilage and Diarthrodial structures. In order for people to prevent Osteoarthritis Knee, it is important to identify the risk factors that can lead to the condition. With this understanding, it is hoped that people can avoid the risk factors as part of prevention efforts. By distributing leaflets and conducting Knee Osteoarthritis examinations and daily activity evaluations on patients, the results showed an increase in functional activity in the elderly after undergoing routine exercise therapy. In conclusion, through this counseling activity, increasing public awareness, especially among the elderly, about the importance of preventing Osteoarthritis Knee. Continuous preventive and curative efforts are needed to reduce the incidence rate of Osteoarthritis Knee in the community.

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.

Medical record data can be used as a benchmark and comparison in the health business to ascertain the rate at which a disease is developing in a given area. It would be beneficial, though, if this data could be transformed into useful information, like illness forecasts. Infectious diseases like malaria are common in tropical and subtropical regions. West Sumba Regency is the region with the highest number of malaria cases, and this figure rises year. Of the different Puskesmas labor locations, Lolo Wano Health Center has the largest number of positive cases of malaria. In order to apply information system technology and prevent malaria early, research was done at the Lolo Wano Community Health Center to predict malaria using the Naïve Bayes approach. This is because the Community Health Center does not currently have a malaria prediction system. Six of the 16 features in the patient dataset—a total of 27 patient data—were malaria symptoms. When there are suitable illness indicators, positive predictions are produced using the outcomes of Naïve Bayes computations. Before the patient proceeds with a direct medical evaluation, these anticipated results may be utilized as a provisional approximation. Naïve Bayes, Center, Prediction, Malaria

Typhoid is an acute febrile condition caused by infection with Salmonella enterica bacteria, especially the Salmonella typhi variant. Typhoid fever or what we usually know as typhoid fever. However, this disease can also be caused by other types such as Salmonella paratyphi A, Salmonella typhi B, and Salmonella paratyphi C. Typoid fever or typhus abdominalis is an acute infectious disease of the small intestine with symptoms of fever for one week or more accompanied by disorders of the intestinal tract. digestion and with or without impaired consciousness. Bayes' theorem is a theory of probability conditions that takes into account the possibility of an event (hypothesis) depending on other events (evidence). Future events can be predicted if previous events have occurred. Bayes' theorem is a mathematical equation used in probability and statistics to calculate conditional probabilities. In other words, it is used to calculate the probability of an event based on its relationship to other events. Based on the weight value given by the expert to each patient's typhoid symptom data, from the results of the analysis carried out with the diagnosis results from the consultation, the symptoms are High fever (lasting up to two weeks), Headache, Chills, Skin rash, Muscle and joint pain, Extreme fatigue, Dry cough, Confusion or delirium, Nausea and vomiting, Swollen spleen, Abdominal pain with predicted results for Epidemic Typhus with a value of 76.26%.

Dyspepsia is a gastroduodenal disorder that is often characterized by symptoms such as epigastric pain, burning, bloating, and a feeling of fullness after eating. Treatment of dyspepsia often requires examination by a specialist doctor, which may not always be easily accessible due to distance, cost, or time constraints. Therefore, this study aims to diagnose dyspepsia using the Dempster-Shafer method to identify possible dyspeptic diseases such as GERD, gastritis, dyspepsia, and gastric ulcers based on 16 detected symptoms and 4 different treatments. to make it easier for patients to consult and get an initial diagnosis without having to see a specialist doctor directly. From this study, it is expected to help patients get information on the initial diagnosis of the patient.

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.