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Nelly Indriani Widiastuti
Universitas Komputer
Computer Science & IT

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Web Content Mining Menggunakan Partitional Clustering K-Means Pada News Aggregator Baidowi, Achmad Thoriq; Widiastuti, Nelly Indriani
Jurnal Sistem Komputer Vol 5, No 2 (2015)
Publisher : Jurnal Sistem Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jsk.v5i2.85

Abstract

News aggregator is one type of aggregator system (collector) which collects news from various sources, and then presented back to the user in a single entity so that users no longer need to venture out to various news sites for just looking for information. The system requires a news aggregator a way to show the same news information from the websites of news services. Based on that, this paper used  Web Content Mining (WCM) for information retrieval news from online news sites and partitional K-Means clustering system for processing news aggregator in objective the system to display collection of information based on keyword input from the user. From the test results using confusion matrix methods with a number of documents as many as 132 documents were taken from crawling indicate that the method partitional clustering K-Means can be applied to a system news aggregator for classifying news information with keyword "education" with an average accuracy of the classification of 98%. 
FUZZY LOGIC DAN LEXICAL CHAINS UNTUK PERINGKASAN TEKS OTOMATIS Afnan, Wulan Kamilia; Widiastuti, Nelly Indriani
Jurnal Sistem Komputer Vol 7, No 1 (2017)
Publisher : Jurnal Sistem Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jsk.v7i1.130

Abstract

Automatic text summarization (ATS) is a process that was required to produce a summary of a text that contains information with help from a computer. This system is needed to determine the subject matter of a document so that readers can quickly understand. ATS systems need to process the documents resulting in important sentences from the document. In this study, lexical chains were used to generate optimal value for the candidate most powerful word in each sentence. The document is extracted to produce features such as sentence length, the weight of the sentence, the position of the sentence, and the similarity between sentences. The value of the strongest chain will be combined with fuzzy parameters. These features Fuzzy logic predicted the results of a summary of the values of the parameters to be grouped based on the value of linguistic important and unimportant. Furthermore, the final value of the fuzzy will determine the final outcome text summary of the document that was input by the user lexical chains. Testing conducted by manual sentence summary results sourced from respondents and a summary of the results of the ATS system recall, precision and F-measure. Based on the results of research that include the step of determining the problem, the analysis to implementation and testing that has been done before, it can be concluded that the results of the implementation of the method of lexical chains with fuzzy logic for automatic text summarization achieve fairly good.
Co-Authors Afnan, Wulan Kamilia Baidowi, Achmad Thoriq Dewi, S.Pd., M.Si, Kania Evita Muhammad Rivai Rainarli, Ednawati