Comparative Analysis of Text Mining Results With Tf ldf Features and SQL Like Operator in Indonesian News Search Analisa Perbandingan Hasil Text Mining Dengan Fitur Tf ldf dan SQL Like Operator Pada Pencarian Berita berbahasa Indonesia
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Abstract
Research on the implementation of text mining uses the TF IDF method to be used in the Information retrieval / Indonesian news search feature. The dataset used was sourced from NewsAPI and built a Codeigniter based website named "News Plus Six Dua". This study also uses the Vertor Space Model (VSM) method to overcome the weaknesses of the TF IDF method at the time of the sorting process. The results of this study explain that the search by the TF IDF method has higher accuracy when compared to SQL like operators. TF IDF produces a percentage of precision 100% and recall (sensitivity) 66.7% on searches with the keyword "Indonesian soccer schedule" while SQL like operators do not display search results or equal to 0%. But the TF IDF method has the disadvantage of running slower than SQL like operators. This has been tested using either the number of words or terms entered, the number of datasets, and the location of access. At the location of access, access via hosting is monitored faster when compared via localhost.
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Copyright (c) 2020 Riwa Rambu Hada Enda, Fajar Hariadi
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