During the Covid-19 pandemic, teachers and students carried out the online teaching and learning process from home. Distance learning has several problems including the limitations of teachers and students in the world of information and communication technology, the facilities and infrastructure they have, and environmental conditions that are less supportive. The use of laptops and the internet every day are used by students and teachers as the main means of the online teaching and learning process. Continuous use without proper maintenance and lack of knowledge in overcoming the problem of damage to laptops makes teachers and students unable to identify the location of the damage and how to deal with it. Therefore, this expert system application was created to assist teachers and students in detecting the symptoms of laptop damage experienced and solutions to overcome the damage. In the development of this expert system using the Naive Bayes method, this method only requires a small amount of training data to determine parameter estimates during the classification process. The results of the application of the nave Bayes method produce appropriate calculations based on the symptoms of damage and a predetermined list of damage so that it can make it easier for users when analyzing the beginning by using existing symptoms with a system that has been built with very efficient time and has an accuracy rate of 100%.