Klasifikasi Opini Masyarakat Terhadap Naturalisasi Pemain Sepak Bola Menggunakan KNN dan SMOTE - Rikky, Rikky; Graciela, Michelle; Irsyad, Hafiz (Pengelola Jurnal Politeknik Negeri Ketapang, 2024)
This study analyzes public sentiment toward the naturalization of football players using the K-Nearest Neighbor (KNN) method and the Synthetic Minority Oversampling Technique (SMOTE). KNN is employed for sentiment classification, while SMOTE addresses class imbalance in the dataset. The methodology includes data collection, labeling, cleaning, preprocessing, classification, and model evaluation using Google Colab and Python. The results indicate that without SMOTE, the model performs better, achieving high precision, recall, F1 score, and accuracy. In contrast, applying SMOTE reduces performance, particularly in precision and F1 score. The