Analisis Sentimen Publik Terhadap Keberadaan Juru Parkir Liar Menggunakan Naïve Bayes Dengan Teknik SMOTE - Daniel, Daniel; Saputra, Andreas; Al Rivan, Muhammad Ezar (Program Studi Teknik Informatika Universitas Multi Data Palembang, 2024)
The continuous growth of YouTube is increasingly leveraged by users to convey information, including critiques and suggestions about illegal parking attendants. The method used in this research is data classification using the Naïve Bayes Classifier (NBC). The system is developed using internal data collected from the internet/YouTube to determine whether sentences are positive or negative opinions. This determination is classified as a classification process. The data is processed using SMOTE to balance the dataset, followed by classifying comments into two classes: positive and negative. This classification employs the Naïve Bayes algorithm. This classification provides convenience for users to view both positive and negative opinions. The accuracy test results for the Naïve Bayes method without SMOTE for classification yielded an average of 86.93%, while the accuracy test results for the Naïve Bayes method with SMOTE technique yielded an average of 91.99%.