Peringkasan Teks Otomatis Berita Menggunakan Metode Maximum Marginal Relevance

  • Robi Robiyanto Universitas Islam Al-Ihya
  • Nunu Nugraha
  • Ipnu Apriatna

Abstrak

Development of Internet technology affects the increasing number of Indonesian language news website and creates an explosion of information. It requires all the information that can be accessed quickly and does’n require a lot of time in reading a news headline. Automatic text summary technology offers a solution to help search us news content in the form of a brief description (summary). The study begins with a five-stage preprocessing text: solving sentence, case folding, tokenizing, filtering, and stemming. The next process is computer tf-idf weighting, weighting query relevance and similarity weights. Summary results from the extraction using the maximum sentence of marginal relevance. Marginal relevance maximum extraction method is the method used to reduce redundancy in multi ranking sentence on the document.

Keywords : Summary, text preprocessing, tf-idf, query relevance, similarity, maximum marginal relevance

Referensi

] (Widhiarta., S.Kom, Pemrograman Internet 1,

STIMIK AMIKOM Yogyakarta, Yogyakarta,

)

] (Akhmad Sofwan. Komunitas eLearning

IlmuKomputer, STMIK Budi Luhur, Copyright

© 2003-2007 IlmuKomputer.Com)

] (Triswansyah Yuliano, Pengenalan PHP,

IlmuKomputer.com, Copyright © 2003-2007)

] Donald Bell, UML basics: An introduction to the

Unified Modeling Language, IBM Rational

Software, 2003)

] (Al Bahra Bin Ladjamudin, Siklus hidup (Life

Cycle) dengan model-model waterfall 2006: 18)

] (Arief Ramdhan, Seri Pelajaran Komputer

Internet dan Aplikasinya, Elex Media

Komputindo, Jakarta, 2005)

] (Sr. Maria Assumpta Rumanti, Dasar – Dasar

Public Relations, Grasindo, Jakarta 2002)

] (Budi Permana, S.Kom, Cepat Mahir Bahasa

Pemrogramanepat Mahir Bahasa

Pemrograman PPH, IlmuKomputer.com,

Copyright © 2003-2013)

] (Roger S.Pressman, Ph.D, Pengertian White

box- Black box 2010)

] (Rochayah Machali, Pedoman Bagi

Penerjemah, Mizan Pustaka, Bandung 2009)

] (Krismiaji, dalam bukunya yang berjudul Sistem

Informasi Akuntansi (2010:71)

] Hovy, E. and Lin, C. Y. (1999). Automated text

summarization in summarist. In Mani, I. and

Maybury, M. T., editors, Advances in Automatic

Text Summarization, pages 81-94. MIT Press

] Xie, Shasha. 2010. Automatic Extractive

Summarization Meeting Corpus. Dissertation.

Dallas: The University of Texas at Dallas.

] Grossman, D., dan Ophir, F. 1998. Information

Retrieval: Algorithm and Heuristics. Kluwer

Academic Publisher.

] Golstein, Jade and Carbonell, Jaime. 1998.

Summarization: Using MMR for Diversity

Based-Reranking and Evaluating Summaries.

Langauge Technologies Istitute. Carnegie

Mellon University.

] Erwin A.H., Muhammad. 2005. Sistem

Pengidentifikasi Otomatis Pokok Kalimat Suatu

Paragraf Dalam Dokumen Ekspositori Dengan

Model Ruang Vektor. Laboratorium

Pemrograman dan Informatika Teori.

Yogyakarta: Jurusan Teknik Informatika

Fakultas Teknologi Industri Universitas Islam

Indonesia.

] (Sparck dan Galliers, konsep Dasar Metode

,

] (Iyan Sammerville, Software Enginering

(Rekayasa Perangkat Lunak) Edisi 6 Jilid 1,

Erlangga, Jakarta, 2003)

Diterbitkan
2019-05-06
Bagian
Articles