2020
DOI: 10.18860/mat.v12i2.8372
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Sistem Automatic Text Summarization Menggunakan Algoritma Textrank

Abstract: Text summarization (perangkuman teks) adalah pendekatan yang bisa digunakan untuk meringkas atau memadatkan teks artikel yang panjang menjadi lebih pendek dan ringkas sehingga hasil rangkuman teks yang relatif lebih pendek bisa mewakilkan teks yang panjang. Automatic Text Summarization adalah perangkuman teks yang dilakukan secara otomatis oleh komputer. Terdapat dua macam algoritma Automatic Text Summarization yaitu Extraction-based summarization dan Abstractive summarization. Algoritma TextRank merupakan alg… Show more

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Cited by 6 publications
(10 citation statements)
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“…In the present study, an equal number of male and female patients were operated, with the majority of patients belonging to the age group 40 to 50 years, with the most common occupation among patients being construction workers. This is in contrast with studies conducted by Elzohairy et al, 9 Lange and Hansen, 10 Zamzam, 11 all of which found male preponderance among patients with pelvic ring injuries, with road traffic accidents being the most frequent mode of injury. This may be explained by the fact the majority of patients presenting to our center came from neighboring rural areas, working at construction sites (both males and females).…”
Section: Discussioncontrasting
confidence: 89%
“…In the present study, an equal number of male and female patients were operated, with the majority of patients belonging to the age group 40 to 50 years, with the most common occupation among patients being construction workers. This is in contrast with studies conducted by Elzohairy et al, 9 Lange and Hansen, 10 Zamzam, 11 all of which found male preponderance among patients with pelvic ring injuries, with road traffic accidents being the most frequent mode of injury. This may be explained by the fact the majority of patients presenting to our center came from neighboring rural areas, working at construction sites (both males and females).…”
Section: Discussioncontrasting
confidence: 89%
“…Pada langkah ini, peneliti menggunakan library dari python yaitu nltk atau dikenal dengan Natural Language Toolkit library. Selanjutnya, hasil pemecahan menjadi kalimat kemudian diproses kedalam bentuk huruf kecil (lowercase) atau disebut dengan proses case folding [18]. Setelah itu, karakter angka dan non-alphanumeric pada setiap kalimat akan dibersihkan/dihilangkan, seperti karakter !, <, <=, dan lainnya.…”
Section: Tahap Prapemrosesanunclassified
“…Proses berikutnya ialah word tokenizing. Word tokenizing: merupakan tahap preproses dengan memisahkan kalimat menjadi kata-kata [18]. Kalimat dipisah dengan pemisah karakter spasi.…”
Section: Tahap Prapemrosesanunclassified
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