Penerapan naive bayes classifier menggunakan fitur ekstraksi n-gram untuk mendeteksi review spam bahasa Indonesia
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Institusion
Institut Teknologi Telkom Purwokerto
Author
Mukti, Setyaji
Subject
T Technology (General)
Datestamp
2021-04-26 03:47:27
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.
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.
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