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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Cik Feresa Mohd Foozy
Applied Computing Technology (ACT) Universiti Tun Hussein Onn Malaysia (UTHM)

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YouTube Spam Comment Detection Using Support Vector Machine and K–Nearest Neighbor Aqliima Aziz; Cik Feresa Mohd Foozy; Palaniappan Shamala; Zurinah Suradi
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 2: November 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i2.pp612-619

Abstract

Social networking such as YouTube, Facebook and others are very popular nowadays. The best thing about YouTube is user can subscribe also giving opinion on the comment section. However, this attract the spammer by spamming the comments on that videos. Thus, this study develop a YouTube detection framework by using Support Vector Machine (SVM) and K-Nearest Neighbor (k-NN). There are five (5) phases involved in this research such as Data Collection, Pre-processing, Feature Selection, Classification and Detection. The experiments is done by using Weka and RapidMiner. The accuracy result of SVM and KNN by using both machine learning tools show good accuracy result. Others solution to avoid spam attack is trying not to click the link on comments to avoid any problems.
Co-Authors Aqliima Aziz Palaniappan Shamala Zurinah Suradi