Event logs, the primary data source in process mining, consist of events with a case identifier, an activity, and a timestamp. Translucent event logs extend this by also recording enabled activities alongside executed ones. Such data can be extracted from user interactions or running information systems. Fitness, a quality measure in conformance checking, assesses how well an event log aligns with a process model. While fitness has been widely studied, no prior work has considered enabled activities. We introduce the first fitness measurement incorporating this information based on the well-established alignment technique. Our generalized, parameterized approach also supports traditional fitness measurement. Through qualitative evaluation, we demonstrate our method’s applicability and provide insights into the implications of parameters. Our quantitative assessment shows limited computational overhead when enabled activities are considered and provides insights into the fitness score of the classical and translucent approaches. Our results indicate that incorporating enabled activities yields appropriate fitness scores compared to traditional methods, enhancing conformance checking accuracy.