A TOPIC-BASED APPROACH FOR RECOMMENDING UNDERGRADUATE THESIS SUPERVISOR USING LDA WITH COSINE SIMILARITY

Laila Rahmatin Nisa; Ardytha  Luthfiarta; Adhitya  Nugraha; Md. Mahadi Hasan; Kang, Andini  Wulandari; Alam Muhammad Huda

doi:10.52436/1.jutif.2025.6.1.4061

Laila Rahmatin Nisa Informatics Engineering Department, Faculty of Computer Science, Universitas Dian Nuswantoro, Indonesia
Ardytha Luthfiarta Informatics Engineering Department, Faculty of Computer Science, Universitas Dian Nuswantoro, Indonesia
Adhitya Nugraha Informatics Engineering Department, Faculty of Computer Science, Universitas Dian Nuswantoro, Indonesia
Md. Mahadi Hasan School of Engineering & Physical Science, North South University, Bangladesh
Kang, Andini Wulandari Informatics Engineering Department, Faculty of Computer Science, Universitas Dian Nuswantoro, Indonesia
Alam Muhammad Huda Informatics Engineering Department, Faculty of Computer Science, Universitas Dian Nuswantoro, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2025.6.1.4061

Keywords: Google Scholar, Latent Dirichlet Allocation, ResearchGate, Thesis advisor, Topic modeling

Abstract

The thesis is one of the critical factors in determining student graduation. While working on the thesis, students will be guided by a lecturer who has the role and responsibility to ensure that students can prepare the thesis well so that the thesis is ready to be tested and is of good quality. Therefore, selecting a supervisor with the same expertise as the thesis topic is essential in determining students' success in completing their thesis. So far, the selection of thesis supervisors at Dian Nuswantoro University still needs to be done manually by students, so the lack of information about the supervisor can hinder students in determining the supervisor. This study aims to model the topic of lecturer research publications taken from the ResearchGate and Google Scholar platforms so that it is easier for students to choose a thesis supervisor whose research topic is relevant to the student's thesis using the Latent Dirichlet Allocation method. The LDA method will mark each word in the topic in a semi-random distribution. It will calculate the probability of the topic in the dataset and the likelihood of the word against the topic for each iteration. The results of LDA modeling present six main topics of lecturer research with the highest coherence score of 0.764, and then the resulting topics and thesis titles will be compared using cosine similarity. Students can use The highest cosine value as a reference when determining the right thesis topic. Thus, the supervisor selection process will be more focused and in accordance with the student's research interests.

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