Work

Comparison of Latent Semantic Analysis (LSA) and Doc2Vec Algorithms of Thesis Similarity Detection

Year: 2024

Type: article

Abstract: This study aims to develop a system for detecting similarities in thesis titles and content to prevent plagiarism and support student originality. The high level of similarity in final projects is a significant concern in academic environments. Two text vectorization methods, Latent Semantic Analysis (LSA) and Doc2Vec, were compared to measure document similarity. Results showed that LSA achieved a very high cosine similarity (99.94%) due to dimensionality reduction that preserved semantic correlations. In contrast, Doc2Vec produced lower similarity scores, with 7.17% for PV-DM and 39.07% for PV-DBOW, indicating richer text representations. This study adopted the CRISP-DM model, which includes Business Understanding, Data Understanding, Data Preparation, Modelling, and Evaluation. The model is expected to strengthen academic integrity and encourage valuable scientific contributions.

Source: PIKSEL Penelitian Ilmu Komputer Sistem Embedded and Logic

DOI: 10.33558/piksel.v12i2.9954

Authors: Rita Wahyuni Arifin, Mardi Yudhi Putra, Dwi Ismiyana Putri

Institution: Universitas Bina Insani

Language: English

Topic: Academic integrity and plagiarism

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

Open Access status: diamond

OpenAlex ID: W4403800995

FWCI:

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