KLASIFIKASI SENTIMEN DATA TIDAK SEIMBANG MENGGUNAKAN ALGORITMA SMOTE DAN K-NEAREST NEIGHBOR PADA ULASAN PENGGUNA APLIKASI PEDULILINDUNGI - Sheila Gabriela Barus (2022)
One of the government's methods in dealing with the spread of Covid-19 that occurred in Indonesia is to create an application, namely the PeduliLindungi application. This application functions in tracking and monitoring the spread of Covid-19, therefore many Indonesian people must have this application. Many reviews are also given on this application, from positive comments to negative comments. These reviews are used as data in this study to determine the results of community sentiment and to test the classification of the K-Nearest Neighbor algorithm. Data collection was done by scraping on google play using the Python programming language, where the data obtained got 750 negative labels and 250 positive labels. So this unbalanced data must be balanced with SMOTE undersampling and oversampling techniques. Therefore, this study carried out three experiments, namely from unbalanced data, data that had been undersampled and data that had been oversampled with SMOTE. The results of the three experiments obtained the best value using the SMOTE technique at K = 1 with an accuracy value of 0.9766, a precision value of 0.9691, an F1 score of 0.9781, a specificity value of 0.9645, and a sensitivity value of 0.9874.