Peringkas Teks Otomatis Berita Online Komisi Pemilihan Umum Menggunakan Algoritma K-Means Clustering - Ezra Matthew Warouw Runturamby; Vivi Peggie Rantung; Kristofel Santa (Prosiding SISFOTEK, 2024)
This research aims to develop an automatic text summarization system capable of summarizing online news about the General Election Commission (KPU) using the K-Means Clustering algorithm. In the current digital era, online news has become a primary source of information for the public, but the overwhelming amount of available information often makes it difficult for readers to filter and comprehend news efficiently. The low reading interest of the public further exacerbates this issue. Therefore, the automatic text summarization system is expected to provide a solution by helping readers quickly and effectively grasp the essence of the news. The K-Means Clustering algorithm will group sentences in the news into several clusters, which will then be used to create a representative summary. This research also identifies challenges such as the accuracy of the summary and the diversity of language in the news. The implementation of this system is expected to improve readers' time efficiency, provide better access to information, and support increased public participation in the democratic process.