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.
 
 

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.