The rapid development of technology in the globalization era has significantly impacted various aspects of life, including the healthcare sector. RSU Bidadari Binjai, as a healthcare provider, faces challenges in diagnosing and preventing Gastroesophageal Reflux Disease (GERD), a condition with high prevalence and serious complications such as Barrett’s esophagus and esophageal cancer. Therefore, a predictive system capable of early detection is needed to ensure quicker and more effective medical intervention. This research develops a computer-based predictive system using the backpropagation method in artificial neural networks to assist in diagnosing GERD by processing patient symptom data. The system's test results show an accuracy rate of 100% in predicting GERD complications based on the given symptoms, thus supporting more timely and accurate medical interventions.
 
 

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
 
 

This study was conducted at SD Muhammadiyah Sei Cabang, a private school in Langkat Regency, which still uses manual examination methods with identical questions for all students. This method is considered ineffective because it allows students to cheat, leading to exam results that do not accurately reflect their abilities. To address this issue, the study developed a web-based examination system using the Linear Congruent Method (LCM) to generate randomized question numbers. With LCM, each student receives a different set of question numbers, making the exam process more effective and fair. The system was built using PHP programming language and MySQL database, enabling efficient data storage and processing. The implementation of this system resulted in improved accuracy in assessment and reduced cheating potential during exams at the school.