{"http:\/\/lib.itenas.ac.id\/kti\/?p=4238":{"http:\/\/www.w3.org\/1999\/02\/22-rdf-syntax-ns#type":[{"type":"uri","value":"http:\/\/rdfs.org\/sioc\/ns#Post"},{"type":"uri","value":"http:\/\/rdfs.org\/sioc\/types#BlogPost"}],"http:\/\/purl.org\/dc\/elements\/1.1\/title":[{"type":"literal","value":"IMPLEMENTASI MAXIMUM MARGINAL RELEVANCE DAN MATRIKS COSINE SIMILARITY PADA APLIKASI PERINGKASAN DOKUMEN"}],"http:\/\/purl.org\/dc\/terms\/identifier":[{"type":"literal","value":"4238","datatype":"http:\/\/www.w3.org\/2001\/XMLSchema#integer"}],"http:\/\/purl.org\/dc\/elements\/1.1\/modified":[{"type":"literal","value":"2015-03-30","datatype":"http:\/\/www.w3.org\/2001\/XMLSchema#date"}],"http:\/\/purl.org\/dc\/elements\/1.1\/created":[{"type":"literal","value":"2015-03-30","datatype":"http:\/\/www.w3.org\/2001\/XMLSchema#date"}],"http:\/\/rdfs.org\/sioc\/ns#link":[{"type":"uri","value":"http:\/\/lib.itenas.ac.id\/kti\/?p=4238"}],"http:\/\/rdfs.org\/sioc\/ns#has_creator":[{"type":"uri","value":"http:\/\/lib.itenas.ac.id\/kti\/?author=6#account"}],"http:\/\/rdfs.org\/sioc\/ns#has_container":[{"type":"uri","value":"http:\/\/lib.itenas.ac.id\/kti\/#posts"}],"http:\/\/purl.org\/dc\/elements\/1.1\/abstract":[{"type":"literal","value":""}],"http:\/\/purl.org\/rss\/1.0\/modules\/content\/encoded":[{"type":"literal","value":"<![CDATA[Automatic Text Summarization are sorting paragraphs into shorter forms using a computer operated\u00a0applications. This automatic summarization technique works in a computer to summarize the text inputted by\u00a0user. A document entered into the computer application, then processed and summarized to produce a summary\u00a0from original text. In research document extraction method used Maximum Marginal Relevance algorithm.\u00a0MMR is a summary document extraction method is used to summarize a single document or multiple\u00a0documents. MMR summarizing document by calculating the similarity between the sentences text in a\u00a0paragraph. In this summarization contents for document segmentation process is carried out using a combination\u00a0of gender-based matrix cosine similarity. Cosine similarity is used to calculate the relevance of the query\u00a0approach on a document. The determination of relevance in a query against document is considered as\u00a0measurement of similarity between queries with vectors of documents. The results from the automatic summary\u00a0application is a sequential list words according to the results obtained algorithms. The closeness results with\u00a0query assessed from the lambda limit values applied in the cosine similarity is 0 to 1.\r\n\r\nDibuat oleh :\u00a0Jasman Pardede,\u00a0Jordy Sinatria\r\nE-mail :\u00a0jasman@itenas.ac.id\r\nKata kunci :summarization, MMR, queries, similarity, cosine similarity.\r\n\r\nKeterangan : Karya Ilmiah ini\u00a0 dimuat pada Prosiding Konferensi Nasional Sistem Informasi KNSI 2015, 26 - 28 Februari 2015\r\n\r\n<a href=\"http:\/\/lib.itenas.ac.id\/kti\/wp-content\/uploads\/2015\/03\/Text_Summarization_Maximum_Marginal_Relevance_Jasman_Pardede.pdf\">IMPLEMENTASI MAXIMUM MARGINAL RELEVANCE DAN MATRIKS COSINE\u00a0SIMILARITY PADA APLIKASI PERINGKASAN DOKUMEN<\/a>]]>","datatype":"http:\/\/www.w3.org\/1999\/02\/22-rdf-syntax-ns#XMLLiteral"}],"http:\/\/rdfs.org\/sioc\/ns#content":[{"type":"literal","value":"Automatic Text Summarization are sorting paragraphs into shorter forms using a computer operated\u00a0applications. This automatic summarization technique works in a computer to summarize the text inputted by\u00a0user. A document entered into the computer application, then processed and summarized to produce a summary\u00a0from original text. In research document extraction method used Maximum Marginal Relevance algorithm.\u00a0MMR is a summary document extraction method is used to summarize a single document or multiple\u00a0documents. MMR summarizing document by calculating the similarity between the sentences text in a\u00a0paragraph. In this summarization contents for document segmentation process is carried out using a combination\u00a0of gender-based matrix cosine similarity. Cosine similarity is used to calculate the relevance of the query\u00a0approach on a document. The determination of relevance in a query against document is considered as\u00a0measurement of similarity between queries with vectors of documents. The results from the automatic summary\u00a0application is a sequential list words according to the results obtained algorithms. The closeness results with\u00a0query assessed from the lambda limit values applied in the cosine similarity is 0 to 1.\r\n\r\nDibuat oleh :\u00a0Jasman Pardede,\u00a0Jordy Sinatria\r\nE-mail :\u00a0jasman@itenas.ac.id\r\nKata kunci :summarization, MMR, queries, similarity, cosine similarity.\r\n\r\nKeterangan : Karya Ilmiah ini\u00a0 dimuat pada Prosiding Konferensi Nasional Sistem Informasi KNSI 2015, 26 - 28 Februari 2015\r\n\r\nIMPLEMENTASI MAXIMUM MARGINAL RELEVANCE DAN MATRIKS COSINE\u00a0SIMILARITY PADA APLIKASI PERINGKASAN DOKUMEN"}],"http:\/\/rdfs.org\/sioc\/ns#topic":[{"type":"uri","value":"http:\/\/lib.itenas.ac.id\/kti\/?cat=1"},{"type":"uri","value":"http:\/\/lib.itenas.ac.id\/kti\/?cat=7"},{"type":"uri","value":"http:\/\/lib.itenas.ac.id\/kti\/?tag=cosine-similarity"},{"type":"uri","value":"http:\/\/lib.itenas.ac.id\/kti\/?tag=mmr"},{"type":"uri","value":"http:\/\/lib.itenas.ac.id\/kti\/?tag=queries"},{"type":"uri","value":"http:\/\/lib.itenas.ac.id\/kti\/?tag=similarity"},{"type":"uri","value":"http:\/\/lib.itenas.ac.id\/kti\/?tag=summarization"}],"http:\/\/rdfs.org\/sioc\/ns#attachment":[{"type":"uri","value":"http:\/\/lib.itenas.ac.id\/kti\/wp-content\/uploads\/2015\/03\/Text_Summarization_Maximum_Marginal_Relevance_Jasman_Pardede.pdf"}]},"http:\/\/lib.itenas.ac.id\/kti\/?author=6#account":{"http:\/\/www.w3.org\/2000\/01\/rdf-schema#seeAlso":[{"type":"uri","value":"http:\/\/lib.itenas.ac.id\/kti\/?author=6&feed=lhrdf&format=json"}]},"http:\/\/lib.itenas.ac.id\/kti\/?cat=1":{"http:\/\/www.w3.org\/2000\/01\/rdf-schema#seeAlso":[{"type":"uri","value":"http:\/\/lib.itenas.ac.id\/kti\/?cat=1&feed=lhrdf&format=json"}]},"http:\/\/lib.itenas.ac.id\/kti\/?cat=7":{"http:\/\/www.w3.org\/2000\/01\/rdf-schema#seeAlso":[{"type":"uri","value":"http:\/\/lib.itenas.ac.id\/kti\/?cat=7&feed=lhrdf&format=json"}]},"http:\/\/lib.itenas.ac.id\/kti\/?tag=cosine-similarity":{"http:\/\/www.w3.org\/2000\/01\/rdf-schema#seeAlso":[{"type":"uri","value":"http:\/\/lib.itenas.ac.id\/kti\/?tag=cosine-similarity&feed=lhrdf&format=json"}]},"http:\/\/lib.itenas.ac.id\/kti\/?tag=mmr":{"http:\/\/www.w3.org\/2000\/01\/rdf-schema#seeAlso":[{"type":"uri","value":"http:\/\/lib.itenas.ac.id\/kti\/?tag=mmr&feed=lhrdf&format=json"}]},"http:\/\/lib.itenas.ac.id\/kti\/?tag=queries":{"http:\/\/www.w3.org\/2000\/01\/rdf-schema#seeAlso":[{"type":"uri","value":"http:\/\/lib.itenas.ac.id\/kti\/?tag=queries&feed=lhrdf&format=json"}]},"http:\/\/lib.itenas.ac.id\/kti\/?tag=similarity":{"http:\/\/www.w3.org\/2000\/01\/rdf-schema#seeAlso":[{"type":"uri","value":"http:\/\/lib.itenas.ac.id\/kti\/?tag=similarity&feed=lhrdf&format=json"}]},"http:\/\/lib.itenas.ac.id\/kti\/?tag=summarization":{"http:\/\/www.w3.org\/2000\/01\/rdf-schema#seeAlso":[{"type":"uri","value":"http:\/\/lib.itenas.ac.id\/kti\/?tag=summarization&feed=lhrdf&format=json"}]},"http:\/\/lib.itenas.ac.id\/kti\/wp-content\/uploads\/2015\/03\/Text_Summarization_Maximum_Marginal_Relevance_Jasman_Pardede.pdf":{"http:\/\/www.w3.org\/2000\/01\/rdf-schema#seeAlso":[{"type":"uri","value":"http:\/\/lib.itenas.ac.id\/kti\/?attachment_id=4239&feed=lhrdf&format=json"}]}}