Journal Article Plagiarism Detection using Latent Semantic Analysis (LSA) - Navaro, Kamal Fauzan; Wardana, Septiyawan Rosetya; Hapsari, Rinci Kembang (Institut Teknologi Adhi Tama Surabaya, 2023)
Nowadays, plagiarism is no longer strange for university students, especially students. The problem faced is minimizing the occurrence of plagiarism among students, which continues to increase daily. Apart from that, plagiarism also violates academic ethics and can reduce student competence. Apart from that, plagiarism is included in stealing other people's written work. Therefore, plagiarism must be stopped immediately. One solution to this problem is to create an application that can detect document plagiarism effectively and efficiently. This application is expected to reduce the occurrence of plagiarism by detecting similarities in documents, one of which is a journal. This plagiarism detection application was built using the Latent Semantic Analysis (LSA) method. Based on the description above, a Document Similarity Analysis for Journal Plagiarism Detection Using Web-Based LSA was created. System testing was carried out to measure the performance of the Latent Semantic Analysis (LSA) method used in the system on 200 training data and 20 testing data, resulting in an average accuracy percentage of 87.88%. The percentage of accuracy obtained is quite large, so the system created is quite good.