Plagiarism Detection through Internet using Hybrid Artificial Neural Network and Support Vectors Machine
Abstract: Currently, most of
the plagiarism detections are using similarity measurement techniques.
Basically, a pair of similar sentences describes the same idea. However, not
all like that, there are also sentences that are similar but have opposite
meanings. This is one problem that is not easily solved by use of the technique
similarity. Determination of dubious value similarity threshold on similarity
method is another problem. The plagiarism threshold was adjustable, but it
means uncertainty. Another problem, although the rules of plagiarism can be
understood together but in practice, some people have a different opinion in
determining a document, whether or not classified as plagiarism. Of the three
problems, a statistical approach could possibly be the most appropriate
solution. Machine learning methods like k-nearest neighbors (KNN), support
vector machine (SVM), artificial neural networks (ANN) is a technique that is
commonly used in solving the problem based on statistical data. This method of
learning process based on statistical data to be smart resembling intelligence
experts. In this case, plagiarism is data that has been validated by experts.
This paper offers a hybrid approach of SVM method for detecting plagiarism. The
data collection method in this work using an Internet search to ensure that a
document is in the detection is up-to-date. The measurement results based on
accuracy, precision and recall show that the hybrid machine learning does not
always result in better performance. There is no better and vice versa. Overall
testing of the four hybrid combinations concluded that the hybrid ANN-SVM
method is the best performance in the case of plagiarism.
Keywords: plagiarism
detection, machine learning, k-nearest neighbors, artificial neural network, support
vector machine
Author: Imam Much Ibnu
Subroto, Ali Selamat
Journal Code: jptkomputergg140021
