TY - JOUR AU - Hairani Hairani AU - Mengas Janhasmadja AU - Abu Tholib AU - Juvinal Ximenes Guterres AU - Yuri Ariyanto PY - 2024/09/03 Y2 - 2025/04/03 TI - Thesis Topic Modeling Study: Latent Dirichlet Allocation (LDA) and Machine Learning Approach JF - International Journal of Engineering and Computer Science Applications (IJECSA) JA - IJECSA VL - 3 IS - 2 SE - Articles DO - https://doi.org/10.30812/ijecsa.v3i2.4375 UR - https://journal.universitasbumigora.ac.id/index.php/IJECSA/article/view/4375 AB - The thesis reports housed in the campus repository have yet to be analyzed to reveal valuable knowledge patterns. Analyzing trends in thesis research topics can facilitate the selection of research topics, aid in mapping research areas, and identify underexplored topics.Therefore, this research aims to model and classify thesis topics using Latent Dirichlet Allocation (LDA) and the Naïve Bayes and Support Vector Machine (SVM) methods. This study employs the LDA method for thesis topic modeling, while SVM and Naïve Bayes are used for classifying these topics. The research results show that LDA successfully modeled five of the most popular thesis topics, namely two related to computer networks, two on software engineering, and one on multimedia. For thesis topic classification, the SVM method demonstrated higher accuracy than Naïve Bayes, reaching 92.80% after the data was balanced using Synthetic Minority Oversampling Technique (SMOTE). The implication of this study is that the topic modeling approach using LDA is able to identify dominant thesis topics. In addition, the SVM classification results obtained better accuracy than Naïve Bayes in the thesis topic classification task. ER -