Klasifikasi Topik Berita Online Bahasa Indonesia Menggunakan Support Vector Machine Berbasis TF-IDF - Pratama, I Gede Angga; Sudiarsa, I Wayan; Wiguna, Dewa Made Artha; Saputra, I Made Dwi Windu; Winantara, I Made Teguh (Journal of Multidisciplinary Inquiry in Science, Technology and Educational Research, 2026)
The development of online media has led to a significant increase in the number of online news stories, necessitating an automated method to efficiently group and label news topics. The manual labeling process is time-consuming and resource-intensive and prone to inconsistencies. Therefore, this study aims to implement and evaluate the Support Vector Machine (SVM) algorithm in automating the labeling of Indonesian-language online news topics. This study uses a quantitative approach with an experimental method on an online news dataset that has undergone text cleaning and preprocessing stages, including normalization, tokenization, stopword removal, and advanced text processing. Document feature representation is performed using the Term Frequency–Inverse Document Frequency (TF-IDF) method to convert text data into high-dimensional numeric vectors. A classification model is built using a Support Vector Machine with a linear kernel and implemented using the Scikit-learn library. The dataset is divided into training data and test data with a ratio of 80% and 20% to evaluate model performance objectively. The test results show that the SVM model is able to achieve an accuracy level of 79.38%, with relatively balanced precision, recall, and F1-score values ​​across most topic classes. Confusion matrix analysis shows that most documents were correctly classified, although errors still occurred in topics with similar contexts. The findings of this study indicate that the combination of TF-IDF and Support Vector Machines is effective for classifying Indonesian online news texts and has the potential to support the development of a digital content management system based on topic labeling automation.