Text

TOPIC MODELLING DAN TOPIC GENERATION PADA REVIEW GAME STEAM DENGAN FINE-TUNING BERTOPIC DAN QWEN2.5

---

Penilaian

---

This study aims to develop and evaluate a Topic Modelling and Topic Generation system using user review data from the Steam platform. The research utilizes the BERTopic model to cluster reviews into topics in an unsupervised manner, and the Qwen2.5 Large Language Model to generate more informative topic representations. Various algorithm combinations covering embedding, dimensionality reduction, and clustering were tested to determine the optimal configuration using evaluation metrics such as C_V, U_Mass, and C_NPMI. Additionally, the quality of topics generated by Qwen2.5 was evaluated through Cosine Similarity scores. The results indicate that certain algorithm combinations produce more accurate and representative topic clusters. The developed system also features a user-friendly interface for result exploration. This research contributes to text review processing in the gaming domain and serves as a foundation for future studies in opinion analysis and natural language processing.

---

Availability

Inventory Code	Barcode	Call Number	Location	Status
2507003344	T175059	T1750592025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

-

Call Number

T1750592025

Publisher

: Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xvii, 184 hlm., ilus., tab.; 29 cm

Language

Indonesia

ISBN/ISSN

-

Classification

004.07

Content Type

-

Media Type

-

Carrier Type

-

Edition

-

Subject(s)

Teknik Informatika

Specific Detail Info

-

Statement of Responsibility

EM

Other version/related

File Attachment

Comments

---

You must be logged in to post a comment