<![CDATA[Source code plagiarism has become a recurring issue in higher education, particularly in programming-related courses where students may copy portions or entire segments of source code from their peers. This situation creates challenges for lecturers in evaluating the originality of student assignments, especially when dealing with a large number of submissions within limited time constraints. Therefore, this study aims to design and develop a web-based source code plagiarism detection application using the Rabin–Karp algorithm to identify and measure the similarity level between programming code documents. The research employed a qualitative approach through observation, interviews, and literature review, while system development followed the Agile methodology. The developed application was tested using source code files written in C++, Java, and Python programming languages. Black-box testing demonstrated that all system functions operated successfully, including file uploading, preprocessing, tokenization, rolling hash generation, fingerprint matching, and similarity calculation. The validity testing results showed similarity percentages ranging from 2.81%–62.70% for C++ files, 17.26%–54.49% for Java files, and 6.35%–34.89% for Python files. These findings indicate that the application can effectively detect similarities between source code documents and support lecturers in identifying potential plagiarism cases. Furthermore, the Rabin–Karp algorithm proved capable of performing similarity analysis efficiently across multiple programming languages with relatively fast processing time.]]>
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