Peringkasan multi-dokumen berita berdasarkan fitur berita dan part of speech tagging

News Feature Scoring (NeFS) is a sentence weighting method that used to weight the sentences in document summarization based on news features. There are several news features including word frequency, sentence position, Term Frequency-Inverse Document Frequency (TF-IDF), and sentences resemblance to the title. The NeFS method is able to select important sentences by calculating the frequency of words and measuring the similarity of words between sentences and titles. However, NeFS weighting method is not enough, because the method ignores the informative word in the sentence. The informative words contained in the sentence can indicate that the sentence is important. This study aims to weight the sentence in news multi-document summarization with news feature and grammatical information approach (NeFGIS). Grammatical information carried by part of speech tagging (POS Tagging) can indicate the presence of informative content. Sentence weighting with news features and grammatical information approach is expected to be able to determine sentence representatives better and be able to improve the quality of the summary results. In this study, there are 4 stages that are carried out including news selection, text preprocessing, sentence scoring, and compilation of summaries. Recall-Oriented Understanding for Gisting Evaluation (ROUGE) is used to measure the summary results with four variants of function; ROUGE-1, ROUGE-2, ROUGE-L, and ROUGE-SU4. Summary results using the proposed method (NeFGIS) are compared with summary results using sentence weighting methods with news feature and trending issue approach (NeFTIS). The NeFGIS method provides better results with increased value for recall functions in ROUGE-1, ROUGE-2, ROUGE-L, and ROUGE-SU4 respectively 20.37%, 33.33%, 1.85%, 23.14%.