PENERAPAN INDOBERT UNTUK ANALISIS SENTIMEN DAN LATENT DIRICHLET ALLOCATION (LDA) UNTUK PEMODELAN TOPIK PADA ULASAN APLIKASI BANK SUMSELBABEL MOBILE

KANASTAIN, KANASTAIN and Firdaus, Mgs Afriyan (2025) PENERAPAN INDOBERT UNTUK ANALISIS SENTIMEN DAN LATENT DIRICHLET ALLOCATION (LDA) UNTUK PEMODELAN TOPIK PADA ULASAN APLIKASI BANK SUMSELBABEL MOBILE. Undergraduate thesis, Sriwijaya University.

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Abstract

Mobile banking is one example of how traditional banking services have transformed into digital banking services thanks to advances in digital technology. Bank SumselBabel Mobile application is the main focus of research to understand user perception. This research aims to analyze sentiment using the IndoBERT and BERT methods as a performance comparison, as well as identify the main topics in the reviews using Latent Dirichlet Allocation (LDA). The review data was collected on October 30, 2024 with a total of 2632 reviews. IndoBERT has superior performance compared to BERT in analyzing sentiment, with an F1-score of 93% versus 88%. This shows IndoBERT's ability to understand the nuances of Indonesian better than BERT. Furthermore, topic modeling using LDA on each sentiment produces a coherence value of 0.4559 for negative sentiment, 0.4058 for neutral, and 0.4114 for positive sentiment.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Sentiment Analysis, mobile banking, IndoBERT, Latent Dirichlet Allocation (LDA), topic modeling, Bank Sumsel Babel.
Subjects:	T Technology > T Technology (General) > T58.6-58.62 Management information systems
Divisions:	09-Faculty of Computer Science > 57201-Information Systems (S1)
Depositing User:	Kanastain kanastain
Date Deposited:	08 Jan 2025 08:46
Last Modified:	08 Jan 2025 08:46
URI:	http://repository.unsri.ac.id/id/eprint/163162

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