Single Document Automatic Text Summarization Ekstraktif Bahasa Indonesia Dengan Metode Term Frequency-Inverse Document Frequency (Tf-Idf)

Pebrianto, Wahyu (2020) Single Document Automatic Text Summarization Ekstraktif Bahasa Indonesia Dengan Metode Term Frequency-Inverse Document Frequency (Tf-Idf). Undergraduate thesis, Politeknik Negeri Jember.

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Abstract

In the era of big data information is growing so rapidly and also continues to spread on the internet, especially information in the form of textual data, we can get that information simply by reading. But usually the more information contained in a textual document, the document also has a longer text, reading a summary is the solution offered. Text summarization is the process of extracting or collecting important information from the original text and presenting the information in summary form (Deepali K and C. Namrata Mahender, 2016), this research focuses on making text summarization by proposing the TF-IDF method and analyzing the results summary. The results of this research The TF-IDF method can produce summaries that actually produce fewer words and filter out common words and select important words. In terms of language tested by language experts, the summary results have an average value of 57.5% which is classified as amenable to summary results that are easily understood. And the summary results still have the same thoughts as the original document has an average value of 47.5% classified as disagree so that the summary results reduce the delivery of the ideas conveyed, and in terms of summary there is still a clear relationship between sentences, has an average a value of 55%, which is classified as agree and this proves the summary by the TF-IDF method, in terms of the sentence still has a clear relationship with other sentences in the summary.

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