Analisis Visual dan Machine Learning untuk Mengukur Validitas Dokumen Akademik Perpustakaan: Studi pada Data Turnitin

<p><em>The issue of plagiarism in academic works is a major concern in higher education as it can undermine academic integrity. This study aims to analyze the distribution of Turnitin results in academic library documents from various study programs at Universitas Nasional, Jakarta. Using a data visualization approach and machine learning algorithms, this research explores the relationship between Turnitin scores and document validity status. The methods used include data visualization through Scatter Plots, Violin Plots, and Box Plots, with these visualizations utilizing Orange Data Mining as the data processing method. Additionally, a logistic regression algorithm is applied to classify documents based on Turnitin scores. Furthermore, the Chi-Square statistical test is implemented to evaluate the significance of the relationship between Turnitin results and document validity status. The findings of this study indicate that Turnitin scores exhibit significant distribution differences among study programs, with notable disparities between valid and invalid documents. Documents from certain study programs tend to have dominant scores in the 20-50% range, which serves as a critical threshold in determining document validity. This study provides in-depth insights into the patterns of academic document validity and offers a data-driven approach to improving the quality of academic evaluation in higher education. Additionally, this research is expected to serve as a foundation for academic policies in strengthening plagiarism detection systems, increasing transparency in evaluation, and promoting the development of more accurate and efficient document validation methods. However, this study has limitations regarding the scope of data used, particularly in terms of study program representation and external factors that may influence Turnitin scores. Further research is needed to examine pedagogical aspects and academic policies that contribute to variations in Turnitin scores across different study programs.</em></p>