SISTEM PENDETEKSI DINI TRANSLATED PLAGIARISM PADA DOKUMEN DIGITAL

• Main Authors: , Yosua Alberth Sir, , Dr.tech. Khabib Mustofa, S.Si., M.Kom
• Format: Theses and Dissertations NonPeerReviewed
• Published: [Yogyakarta] : Universitas Gadjah Mada 2011
• Subjects: ETD
• Online Access: https://repository.ugm.ac.id/90304/; http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=52485
• Institution: Universitas Gadjah Mada

The use of Internet applications, which have already crossed the language border, caused a serious problem such as translated plagiarism. In academic institutions, translated plagiarism is found in various cases, such as: theses, final projects, and papers. In this thesis, we propose an early detection system for translated plagiarism (Indonesian-English) on digital document which based on the revised version of sentence-based detection algorithm. This algorithm is a modified version of the sentence-based detection algorithm. The proposed system works as follows: (i) translating the input document using the Google Translate API component, (ii) searching for PDF documents that are similar to the translated document on WWW repository using the Google AJAX Search API component. If it is found, (iii) the system will download these documents, then (iv) does some preprocessing steps, such as: removing punctuation, removing numbers, removing stopwords, removing repeated words, and doing a process called lemmatization of words. The last process (v) is to compare the content of translated document against downloaded documents. To compare the accuracy of detection, we built two systems: (i) the first system based on sentence-based detection algorithm and (ii) a second system based on the revised version of sentence-based detection algorithm, and then tested both systems by using the same datasets (25 datasets). We evaluate the accuracy of both systems by using RMSE metric and the t test as the basis for comparison. The results showed that there was a significant difference in accuracy between the two systems, where the system based on the revised version of sentence-based detection algorithm (RMSE=24,95%) is more accurate than the system based on sentence-based detection algorithm (RMSE=38,54%).