Analisa Testimonial Wisatawan Menggunakan Text Mining Dengan Metode Naive Bayes dan Decision Tree, Studi Kasus Pada Hotel-Hotel Di Jakarta
The ability to express the opinion of lines of text can be extremely useful, and this is a good
area to be studied, no doubt because of the possibility of commercial value because most information
is now stored as text. In this age of the internet today many reviews, opinions, comments or opinions
are so abundant and scattered in internet media in the form of text, thus giving rise to the term or
overflow of text that can be used as the object of the new knowledge that is what is called Text Mining.
Currently, Text mining is believed to have a high potential commercial value. Text Mining is a process
that aims to find the information or the latest trends previously revealed, to process and analyze large
amounts of data. In analyzing part or all unstructured text, text mining to try to associate one with the
other parts of the text based on certain rules.
Besides text mining is also interpreted as a data mining activities from the data in the form of
text or a document, with the aim of searching for words that can represent what is in the document so it
can be analyzed in text mining connectedness, In the processing Text Mining conducted prior
Tokenizing process, Filtering, Stemming, Tagging and Analyzing. Stages of the process is carried out
with the help of tools Semantria. Results semantria process tool is a classification based sentiment
analysis. After appearing classification sentiment analysis, the next step was measured by the method
of Naive Bayes and Decision Tree. Baselines to generate corresponding processed products is to ensure
the characteristics of the data related to the objectives to be achieved from the study.
In the context in the field of Text Mining There are a variety of processing one of which is with
Process Mining with a focus on the classification.The processed text mining based on sentiment
classification, the region with the sequence that has the highest positive sentiment Central Jakarta
(80.7%) and North Jakarta (71.2%), East Jakarta (65.1%), West Jakarta (65% ) and South Jakarta