Klasifikasi Materi Judul Berita Hasil Siaran TVRI dengan Metode Support Vector Machine
DOI:
https://doi.org/10.47065/bulletincsr.v6i5.973Keywords:
Classification; News Title; TF-IDF; Support Vector Machine; TVRIAbstract
The development of digital technology has driven an increase in the volume of textual data, especially in the news broadcasting sector which demands fast, accurate, and structured information management. TVRI West Sumatra as a public broadcasting institution faces problems in grouping broadcast news titles which are still done manually, potentially causing inefficiencies in information management and retrieval. This study aims to implement the Support Vector Machine (SVM) algorithm to classify TVRI West Sumatra broadcast news titles into political, economic, and social categories. The research stages include text preprocessing consisting of case folding, tokenizing, normalization, stopword removal, and stemming, followed by word weighting using Term Frequency–Inverse Document Frequency (TF-IDF). The dataset used is 900 news titles obtained from the rundown document of the "West Sumatra Today" program during the period January–December 2025. The dataset is divided into 80% training data and 20% test data, then classified using the Support Vector Machine algorithm with a linear kernel. Test results showed that the model achieved an accuracy of 88%, with a weighted average precision of 89%, a weighted average recall of 88%, and an F1-score of 88%. These results demonstrate that the SVM method is able to classify news titles effectively, thereby improving efficiency and consistency in news archive management, and supporting information retrieval processes in public broadcasting institutions.
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