Topic Modeling in Thesis Titles of Students from the Faculty of Economics Universitas Garut Using Latent Dirichlet Allocation Modeling

Fikri Fahru Roji (UNIGA)
Dinar Rahayu (Institut Pendidikan Indonesia)
Riyad Sabilul Muminin (UNIGA)
Dendi Ramdani (UNIGA)
Dede Hendrik (Institut Teknologi Al-Muhajirin)

RISTEC : Research in Information Systems and Technology
Vol 4, No 1 (2023): Riset Sistem dan Teknologi Informasi

In higher education, the completion of a thesis within a 1-year timeframe is a prerequisite for graduation. The selection of a thesis topic is influenced by personal interest, the expertise of the thesis supervisor, and data availability. This research is designed to analyze the thesis topics of Economics Faculty students at Garut University using the Latent Dirichlet Allocation (LDA) Modeling method. Utilizing quantitative and qualitative approaches, this research applies the concept of big data with techniques such as Data Crawling, Data Preprocessing, and Text Mining. The research successfully conducted topic modeling using the LDA method. The analysis showed that topic modeling with the LDA algorithm resulted in seven common thesis topics used in the students' thesis titles. With this, the research contributes to the understanding and efficacy in the determination of students' thesis topics. It is hoped that the results of this research can be utilized to assist in the efficient completion of theses. Keywords: Topic Analysis; Topic Modeling; Thesis Title; Latent Dirichlet Allocation; LDA