TEXT MINING MENGGUNAKAN GENERATE ASSOCIATION RULE WITH WEIGHT (GARW) ALGORITHM UNTUK ANALISIS TEKS WEB CRAWLER | Zendy

Zulkifli Arsyad | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

TEXT MINING MENGGUNAKAN GENERATE ASSOCIATION RULE WITH WEIGHT (GARW) ALGORITHM UNTUK ANALISIS TEKS WEB CRAWLER

Author(s) -

Zulkifli Arsyad

Publication year - 2020

Publication title -

internal (information system journal)

Language(s) - English

Resource type - Journals

eISSN - 2656-0259

pISSN - 2621-9433

DOI - 10.32627/internal.v2i2.86

Subject(s) - web crawler , association rule learning , computer science , weighting , information retrieval , data mining , apriori algorithm , search engine , focused crawler , knowledge extraction , a priori and a posteriori , web mining , information extraction , web page , world wide web , static web page , web navigation , medicine , radiology , philosophy , epistemology

Text mining is widely used to find hidden patterns and information in a large number of semi and unstructured texts. Text mining extracts interesting patterns to explore knowledge from textual data sources. Association rule extraction GARW (Generating Association Rule using Weighting Scheme) can be used to find knowledge from a collection of web content without having to read all the web content manually from the many search results of crawlers. The GARW algorithm is a development of a priori to produce relevant association rules. From the results of this knowledge discovery can facilitate netizens users in finding relevant information from search keywords without having to review one by one web content generated from search engine searches.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research