Performance Improvement of Cosine Similarity Algorithm with Bidirectional Encoder Representations from Transformers on Abstract Document Similarity Detection - Pradana, Musthofa Galih; Irzavika, Nindy; Maulana, Nurhuda; Mu, Jesselyn; Wari, Valtrizt Khalifah (Society of Visual Informatics, 2025)
In thesis courses or final projects, students are required to be able to conduct research by the science they are engaged in, find innovations, solve problems, and foster a culture and critical mindset. However, the issue that is often encountered is plagiarism. Plagiarism is taking a work that can be in the form of someone else's opinion and making it seem as if it is your own. The step in applying technology that can be done is to carry out early detection of the similarity of documents written by students. In this case, the document that will be detected is an abstract that must be collected by students when submitting a thesis title. The algorithm used is a cosine similarity algorithm, which is computationally efficient because of its ease of interpretation and compatibility with large-scale data. This research was carried out using two schematic approaches: bidirectional encoder representations from transformers (BERT) and not bidirectional encoder representations from transformers (BERT). The corpus data used in this study was 1450 data of student thesis abstract documents, with the test using 10 data to see the performance of the cosine similarity algorithm in detecting the similarity of abstract documents. The results showed that documents with optimization using the Bidirectional Encoder Representations from Transformers (BERT) approach had better results, with an average performance improvement of 23.48%.