<![CDATA[Since it was declared a global pandemic in March 2020, Corona Virus Disease (Covid-19) has become the world's attention, a lot of research has focused on all things related to Covid-19. Covid-19 is an infectious disease caused by the acute respiratory syndrome Corona virus 2 (SARS-CoV-2). Several studies have shown that the symptoms of COVID-19 persist for a long period of time even though they have been declared cured of Covid-19, this is known as Long Covid. Complaints that are often experienced by patients who progress to Long Covid are fatigue, headaches, coughs, runny noses, sleep disturbances and even shortness of breath. Several risk factors for the occurrence of Long Covid include age, gender, patient congenital disease, condition during acute infection, ethnicity and the patient's Body Mass Index (BMI). To anticipate the occurrence of long covid, it is necessary to have a risk prediction system for the occurrence of long covid in covid-19 patients, this aims to anticipate and prepare for early handling and prevention efforts for covid-19 patients who are at risk of experiencing long covid. Prediction of the risk of long covid can be done by classifying long covid risk factors by utilizing data mining. The purpose of this study is to classify symptom data and patient history, so that data patterns can be obtained that can be used as predictions to estimate the risk of the occurrence of Long covid-19 in Covid-19 survivors. This study uses the Naïve Bayes classification method by classifying data based on the Long covid risk factor and the feature selection information gain method which is used as a technique in attribute selection to optimize the nave Bayes algorithm. The results of this study have a real contribution to the development of science and technology. The concept of the resulting prediction data pattern can be used as a reference in developing early detection of the risk of the occurrence of Long Covid.]]>
