02994nam a2200301 i 4500 CSPC 20251202102758.0 251202s2024 ph a b 000 0 eng d CSPC eng CSPC rda UTH BSCS D362p 2024 Delos Santos, Venn P. author. Python code similarity detection using abstract syntax tree with k-means algorithm / Venn P. Delos Santos, John Christian M. Gava, and Marc Christian D. Tumaneng 166 leaves. rdacontent text rdamedia unmediated rdacarrier volume Undergraduate Thesis (BSCS) - Computer Science. Camarines Sur Polytechnic Colleges, 2024. Includes bibliographical references. This study presents a hybrid methodology for detecting source code similarity in Python programming language. Integrating Abstract Syntax Tree (AST) analysis for structural similarity evaluation, the SimHash algorithm for textual similarity assessment, and the K-Means clustering algorithm for grouping Python codes according to their similarity fea-tures. The evaluation of the source code similarity detection was thoroughly conducted us-ing a dataset of student-submitted Python code, focusing on the detection of prevalent types of source code changes employed to conceal plagiarism attempts. With 28,701 pairwise comparisons across non-plagiarized and plagiarized code files, the model achieved an ac-curacy of 83%, with a high precision of 100%, a recall of 83%, and a 91% F-Measure. Ad-ditionally, the similarity detection process underwent a comprehensive assessment through test cases involving different degrees of source code transformation, demonstrating the model's robustness in identifying plagiarism across varying levels of structural and textual code transformations. Moreover, the K-Means algorithm exhibited high performance in grouping codes effectively, with optimal cohesion and separation validated using evalua-tion metrics such as the elbow method and silhouette score. Furthermore, a web application prototype was developed using Streamlit to operationalize the methodology. The applica-tion is user-friendly and provides an accessible platform where users can register, log in, and utilize the toolkit for source code similarity detection. The findings of the study under-score the effectiveness of integrating Abstract Syntax Tree (AST) analysis with a text-based approach, combined with the K-Means algorithm, as a scalable and comprehensive method for detecting source code similarity in Python programs. Gava, John Christian M. author. Tumaneng, Marc Christian D. author. Fortuno, Kaela Marie N. adviser. Oñate, Joseph Jessie S. chair, panelist. Benitez, Ian P. panelist. Onesa, Rosel O. panelist. ddc 0 UM D362p BSCS UTH 2024 0 0 ddc 0 0 CSPC CSPC UTH 2025-12-02 0 UTH BSCS D362p 2024 007022UM 2025-12-02 10:28:26 1-1 2025-12-02 UM 30988 30988