The rapid development of information technology has made it easier to access various sources of information, but it has also increased the risk of plagiarism, especially among university students. To maintain academic integrity and the quality of education, plagiarism detection is essential. This study aims to develop a plagiarism detection system using the Knuth-Morris-Pratt (KMP) algorithm combined with the Jaccard Similarity method to measure the similarity between documents. The system is designed to analyze Indonesian-language essay documents in Word (.docx) and PDF (.pdf) formats that do not contain images or tables. The detection process begins with data preprocessing steps, including case folding, tokenizing, stopword removal, and stemming, to enhance efficiency and accuracy. The system is developed using the Python programming language and the Flask framework. Test results show that the system can detect plagiarism with an average accuracy of 98.66%. The level of similarity between documents is then categorized into three levels: Minor, Moderate, and Severe Plagiarism. This system is expected to serve as an effective tool in minimizing plagiarism practices in higher education and supporting the creation of an honest and responsible academic culture.