<![CDATA[Aster Clinic PKBI Garut is one of the healthcare facilities that remains active in providing services for the management and prevention of Sexually Transmitted Infections (STIs). According to experts, the consultation process at this clinic takes approximately 40 minutes per patient, limiting the service capacity to about 9–10 patients per day, or around 35–40 patients per month. This situation results in many patients remaining untreated, despite the fact that STIs require fast and accurate diagnosis and treatment. The purpose of this research is to design an expert system that can assist in the initial diagnosis of STIs at Aster Clinic PKBI Garut. The system is developed by applying the Forward Chaining and Certainty Factor inference methods to generate a diagnosis based on the symptoms reported by the user, while also providing a confidence level for the diagnosis result. The design process uses the Rational Unified Process (RUP) approach, with the knowledge base consisting of local symptom data from Aster Clinic PKBI Garut. The implementation results show that this system successfully provides an autonomous initial diagnosis of STIs and offers relevant initial treatment recommendations. Beta testing, which involved experts and users, demonstrated that the system design has high accuracy, is easy to use, and is suitable for use as an early diagnosis tool. This research has the advantage of utilizing local data and applying an inference method capable of producing more measurable certainty values. The implications of this research include increased public access to early STI detection, efficiency in medical consultation time, and the potential for integrating the system into the clinic’s digital services. The main contribution of this research is the introduction of an expert system that is not only educational and interactive but also provides practical solutions to support technology-based healthcare services.]]>
