Klasifikasi Berita Televisi Menggunakan Metode K-NN, Naïve Bayes dan SVM - Wuryantoro, Tri; Muljono, Muljono; Pujiono, Pujiono (Universitas Budi Darma, 2024)
News through television media is still one of the media that is widely used by the public in obtaining the latest information. The Central Java TVRI Public Broadcasting Institution has a news program called Berita Jawa Tengah which airs every day and doesn’t have a classification system. This research was carried out in several stages, in the initial stage preprocessing was carried out which included: data collection, cleaning, case folding, tokenizing, normalization, stopword removal, stemming, then continued with word weighting (TF-IDF) and finally applying the K-Nearest Neighbor classification method (K-NN), Naïve Bayes and Support Vector Machine (SVM). The results of the classification carried out show that the K-NN classification method has higher results compared to other methods, namely an Accuracy value of 0.94, Precision 0.92, Recall 0.94 and f1-score 0.93, so it can be concluded that Television news classification using the K-NN method is the method that provides the most accurate results.