@thesis{thesis,
		author={Mukti  Setyaji},
		title ={Penerapan naive bayes classifier menggunakan fitur ekstraksi n-gram untuk mendeteksi review spam bahasa Indonesia},
		year={2018},
		url={https://repository.ittelkom-pwt.ac.id/5230/},
		abstract={Reviews have many and varied valuable information that is usually used for 
buyers and sellers to make a decision. Unfortunately, the freedom that is given to user
to make a review have make some irresponsible people to use it for their own fortune
by making review spam. Furthermore, in existing work on extracting review like mining
opinion, have a little awareness about this kind of spam. In this report, will classify
review spam Bahasa by using Naive Bayes Classifier with n-gram. By comparing the
value of n in n-gram, obtain the highest accuracy 80.44% is with n = 1-2 and how big
the effect given by n-gram for classifying review spam Bahasa. Satisfactory results are
obtained with the Naive Bayes Classifier using feature extraction n-gram and how
important to determine the value of n on the n-gram in obtaining a good accuracy.


Keywords: classification, machine learning, naive bayes, opinion, review, supervised
learning, spam, text mining.}
		}