ANALISIS SENTIMEN ULASAN PENGGUNA APLIKASI FINTECH MENGGUNAKAN FRAMEWORK CROSS-INDUSTRY STANDARD PROCESS FOR DATA MINING (CRISP-DM) DALAM PENENTUAN PRIORITAS PENGEMBANGAN PRODUK

AMALSYAH, MUHAMMAD RIZKY and Kurniawan, Dedy (2025) ANALISIS SENTIMEN ULASAN PENGGUNA APLIKASI FINTECH MENGGUNAKAN FRAMEWORK CROSS-INDUSTRY STANDARD PROCESS FOR DATA MINING (CRISP-DM) DALAM PENENTUAN PRIORITAS PENGEMBANGAN PRODUK. Undergraduate thesis, Sriwijaya University.

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Abstract

The rapid growth of fintech applications has increased the need for sentiment analysis to understand user perceptions of the offered products. This study focuses on sentiment analysis of user reviews for the Flipapplication on Google Play Store by applying the Support Vector Machine (SVM)algorithm within the CRISP-DMframework.The analysis process involves text preprocessing, sentiment labeling using a pretrained BERT model, and classification using SVMwith TF-IDF feature extraction. The results indicate that the majority of users express positive sentiment (56.9%), primarily regarding cost efficiency, transaction ease, and product speed. However, negative sentiment (43.1%) is also present, mainly concerning additional fees, transaction delays, and technical issues in app usage. A topic modelling analysis using the Latent Dirichlet Allocation (LDA) method identifies key topics that highlight both Flip's strengths and challenges. The findings suggest that while Flip holds significant potential in meeting user needs, improvements are needed in product aspects, cost transparency, and app performance optimization. This study is expected to serve as a strategic foundation for fintech app developers to enhance data-driven product quality, ultimately increasing user satisfaction and loyalty.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Analisis Sentimen, Fintech, Support Vector Machine, CRISP-DM, Pemodelan Topik.
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 57201-Information Systems (S1)
Depositing User:	Muhammad Rizky Amalsyah
Date Deposited:	18 Mar 2025 02:08
Last Modified:	18 Mar 2025 02:08
URI:	http://repository.unsri.ac.id/id/eprint/169149

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