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  2. Vol 7, No 2 (2023)
  3. Alamsyah

Klasifikasi Gejala Awal Covid-19 dengan Algoritma Classification and Regression Tree (Cart)

Agung Alamsyah, Anita Desiani, Endro Setyo Cahyono

Abstract


COVID-19 is a disease that can cause death and can spread to others. By identifying early symptoms of the disease, early detection can be made for several symptoms that may cause COVID-19. One way to predict COVID-19 is through classification methods. By identifying the symptoms that have an impact on COVID-19, it is hoped that the COVID-19 virus can be stopped from spreading and the world's condition can be normal. This study shows an analysis of attributes that may have an impact on the onset of COVID-19 in an individual. The classification method used is one of the decision tree methods, namely the Classification and Regression Tree (CART). The training and testing methods used in this study are cross-validation and percentage split. The attribute that has a significant influence in this classification using CART method is lung infection. The performance of the system using cross-validation method with a value of k of 10 obtained an accuracy of 85%, which is considered good, while using a percentage split of 66%, an accuracy of 87% was obtained. The evaluation results for the class indicating COVID-19 with precision and recall in cross-validation are 70% and 68%, respectively, while for the percentage split method, precision and recall values of 75% and 70% were obtained, respectively.


Keywords


COVID-19, Classification, Classification and Regression Tree, Decision Tree

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DOI: 10.24269/jkt.v7i2.2095

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