Klasifikasi Daya Tarik Konten Artikel Media Daring Dari Data Google Analytics Dengan C-FDT

Erlin Windia Ambarsari

Abstract


Information of article which had attractive contains as Trending Topics, although this is article hoax or not. The frequency of article's content which created by online media, it can be monitored by Google Analytics. One of the reasons for using Google Analytics is to understand the content of a site which leads to the change and behavior of behind the content. Google Analytics can be regarded as web analytics software with ease of installation. Classification of Google Analytics data with C-Fuzzy Decision Tree (C-FDT), aims to get the attraction of article content, which means having special attention from visitors and the article can be interesting or not, and observed whether C-FDT can recognize patterns from metric data Google Analytics. The purpose of this study is the results of FDT are expected to facilitate online media managers to analyze the content of articles and evaluate content groups tend to potentially gain traffic for getting promotional or marketing advertising as revenue from online media sites. The results obtained are C-FDT can recognize the pattern of Google Analytics metrics thus as facilitating the search of the article content into a simple form that is the reduction of attributes by grouping data with the same object and the data had Pruning. Online media managers can focus on certain attributes that have a big effect on Content Articles. However C-FDT is having trouble dealing with data sync due to system errors when retrieving data from Google Analytics. Therefore it is necessary to monitor data in time series.

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DOI: http://dx.doi.org/10.30591/jpit.v3i2.876

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Creative Commons License
JPIT (Jurnal Informatika: Jurnal Pengembangan IT) is licensed under a Creative Commons Attribution 4.0 International License.