KLASIFIKASI KATEGORI DOKUMEN BERITA BERBAHASA INDONESIA DENGAN METODE KATEGORISASI MULTI-LABEL BERBASIS DOMAIN-SPECIFIC ONTOLOGY

https://doi.org/10.22146/teknosains.8611

Janur Adi Putra(1), Pangestu Widodo(2*), Suwanto Afiadi(3)

(1) Jurusan Teknik Informatika, Fakultas Teknologi Informasi, Institut Teknologi Sepuluh Nopember (ITS) – Surabaya, 60111, Indonesia
(2) Jurusan Teknik Informatika, Fakultas Teknologi Informasi, Institut Teknologi Sepuluh Nopember (ITS) – Surabaya, 60111, Indonesia
(3) Jurusan Teknik Informatika, Fakultas Teknologi Informasi, Institut Teknologi Sepuluh Nopember (ITS) – Surabaya, 60111, Indonesia
(*) Corresponding Author

Abstract


A news document often related  to more than one category,  necessary for utilization  the method of categorization that is not only fast but also able to classify a news into many categories. Many methods can be used to categorize the news documents, one of which is an ontology. Ontology approach in the categorization of a document is based on the similarity of news features in documents with features that exist in the ontology. The use of ontologies in categorization that just based on the occurance of the term in calculating the relevance of the document, led to the emergence of many other features that are actually very relevant is undetectable. This paper proposed a new method for categorizing news documents are related with many categories, the method is based on a specific domain ontology and for document relevance calculation is not only based on the occurrence of the term but also take into account the relationships between terms that are formed. Tests performed on the Indonesian language news document with  two categories: sports and technology. The trial results show the value of the average accuracy is high, that the sports category was 93,85% and the technology category is 96,32%.


Keywords


Categorization; Domain-specific; Multi-label; News document; ontology

Full Text:

PDF


References

Baeza-Yates, R., & Ribeiro-Neto, B. (1999). Modern information retrieval. New York: Addison Wesley.

 Ben-Dov, M., dan Feldman R. 2001, Text Mining and Information Extraction, Chapter 38.

 Husni. IR dan Klasifikasi. Diktat kuliah, Universitas Trunojoyo.

Milton, N. (2003). Knowledge Engineering. November  21,2015.http://www.epistemics.co.uk/Notes/61-0-0.htm

 N.F. Noy, D.L. McGuinness, Ontology Development 101: A Guide to Creating Your First Ontology, Knowledge Systems Laboratory (KSL) of Department of Computer Science Stanford, USA: Technical Report, KSL-01-05, 2001.

 Noy, N.F., & McGuinness, D.L. (2001). Ontology Development 101: A guide to creating your first ontology. Knowledge Systems Laboratory (KSL) of Department of Computer Science Stanford, USA: Technical Report, KSL-01-05.

 Sebastiani, F. (2002). Machine learning in automated text categorization. ACM Computing Surveys, Vol. 34 (1), 1-47.



DOI: https://doi.org/10.22146/teknosains.8611

Article Metrics

Abstract views : 726 | views : 745

Refbacks

  • There are currently no refbacks.




Copyright (c) 2017 januar adi putra, pangestu widodo, suwanto afiadi

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.