<![CDATA[The lack of understanding regarding the normal limits of physiological menstrual parameters leads to delayed detection of reproductive health disorders. Current conventional tracking applications generally focus on date prediction without analyzing accompanying symptoms. This research provides a technical contribution in the form of an expert system design for the early diagnosis of menstrual disorders based on four basic physiological variables: menstrual duration, cycle length, blood volume, and pain symptoms. The system is built using the Forward Chaining method to map the diagnostic inference flow, and the Certainty Factor (CF) to calculate the percentage of the expert's confidence level in the initial medical conclusion. Rule base validation was conducted with a general medical expert as a reference for early-stage screening (Amenorrhea, Oligomenorrhea, Polymenorrhea, Hypermenorrhea, Hypomenorrhea, Dysmenorrhea, and Normal). Black Box functionality testing shows that the system logic runs validly according to the static rule boundaries. Evaluation using the System Usability Scale (SUS) on 30 respondents resulted in a score of 83, indicating that the application has an excellent level of usability. As an early detection prototype, this system focuses on presenting diagnostic probabilities based on expert certainty, although continuous clinical validity testing using a Confusion Matrix remains necessary to measure medical accuracy comprehensively.]]>
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