Peringkasan Teks Ekstraktif Kepustakaan Ilmu Komputer Bahasa Indonesia Menggunakan Metode Normalized Google Distance dan K-means - Dhimas Anjar Prabowo; Mochammad Ali Fauzi; Yuita Arum Sari (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2017)
The yearly rapid increase of digital data surface a problem for a person to be able to read every information that was served. One example of its data was a textual data document, which could be in a form of research document. This problem urges for a solution that is a technique to present all of the information in a clear and concise form, and one of its solution is a text summarization technique. This research proposed a text summarization technique using Normalized Google Distance (NGD) and K-means as its extractive algorithm, with a textual data that is a research document based on computer science studies in an Indonesian language as its research object. NGD will be used as an algorithm to derive sentences that was related to its document's title, and K-means will be used as an algorithm to obtain important sentences by its several topics that occurs in the document. The experiment result showed that this research possess an average best of precision, recall, and relative utility measures scores by 0.27, 0.43, and 0.45 respectively. In the other hand, the experiment result also showed that this research possess an average of kappa measure score by 0.41 or moderate.