Peringkasan Teks Otomatis Pada Artikel Berita Kesehatan Menggunakan K-Nearest Neighbor Berbasis Fitur Statistik - Rachmad Indrianto; Mochammad Ali Fauzi; Lailil Muflikhah (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2017)
Now days, information about healthy has been widely scattered and very easily obtained through the online website. But, within largest information that contain in the text of article make the reader can't understand about contents of the text. So, we need a system that can summarize a text to make easy the reader in understanding the contents of the text. Automatic text summary using k-nearest neighbor based on statistical features can be solution about the problem. Statistical features such as position of a sentence in a paragraph, overall sentence position, numerical data, inverted commas, the length of the sentence and keyword has important influence become parameter in summarization. From testing of statistical features that have been done by using k = 3, this method get result the best value of precision, recall and f -measure on feature set 9 with values 0.75, 0.71 and 0.72. From the test can concluded that the features that have a significant influence on the rise and fall of precision and recall values are position of a sentence in paragraph and sentence overall position. And then, from the test of k variation on the best feature set, we get maximum feature set value when k = 1 with the average value of precision, recall and f-measure of 0.89, 0.74 and 0.81.