Sentiment Analysis of Fintech Application User Reviews using the CRISP-DM Framework for Product Development Prioritization

Muhammad Rizky Amalsyah, Dedy Kurniawan, Ahmad Rifai, Purwita Sari

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 Flip application on Google Play Store by applying the Support Vector Machine (SVM) algorithm within the CRISP-DM framework. The analysis process involves text preprocessing, sentiment labeling using a pretrained BERT model, and classification using SVM with 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 modeling 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.

Keywords


Analisis Sentimen, Fintech, Support Vector Machine, CRISP-DM, Pemodelan Topik.

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References


A. M. Puteri, I. Inanda, R. B. Prasetio, and R. Sanjaya, “Pengaruh Literasi Keuangan dan Literasi Digital terhadap Preferensi Bank Bank Digital di Kalangan Mahasiswa,” Jurnal Publikasi Ilmu Manajemen, vol. 3, no. 4, pp. 16–25, 2024, doi: 10.55606/jupiman.v3i4.4467.

N. Kristi, D. F. Shiddieq, and D. Nurhayati, “Analisis Penerimaan Aplikasi Flip menggunakan Model Unified of Acceptance and use of Technology 3,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 4, no. 2, pp. 685–694, Apr. 2024, doi: 10.57152/malcom.v4i2.1316.

Y. Ngamal and M. A. Perajaka, “Penerapan Model Manajemen Risiko Teknologi Digital di Lembaga Perbankan Berkaca pada Cetak Biru Transformasi Digital Perbankan Indonesia,” Jurnal Manajemen Risiko, vol. 2, no. 2, pp. 59–74, 2022, doi: 10.33541/mr.v2iIV.4099.

Google Play Store, “Jumlah Ulasan Aplikasi Flip pada Google Play Store.”

A. Fadilla, E. Army, Y. Dwi, P. Rustam, A. Indrijawati, and G. T. Pontoh, “Peran Artificial Intelligence dalam meningkatkan Kualitas Audit: Tinjauan Literatur Sistematis,” Jurnal Akuntansi dan Governance, vol. 5, no. 2, pp. 146–165, 2025, doi: 10.24853/jago.5.2.146-165.

A. Syafa’aturrohman, O. Nurdiawan, F. M. Basysyar, and M. Sulaeman, “Naive Bayes meningkatkan Model Analisis Sentimen pada Ulasan Aplikasi DANA di Playstore Indonesia,” Information Management for Educators and Professionals, vol. 9, no. 2, pp. 171–180, 2024, doi: 10.51211/imbi.v9i2.3330.

Y. Basrina, N. Afryansih, and T. Febriani, “Pengembangan Aplikasi Evaluasi Pembelajaran Wizer.Me pada Mata Pelajaran IPS di MTs Darussalam Aryojeding,” Jurnal Pendidikan dan Ilmu Geografi), vol. 8, no. 1, pp. 31–38, 2023, doi: 10.21067/jpig.v8i1.7361.

F. Xaverius, K. Lile, and I. Suharjo, “Optimalisasi Perbandingan Algoritma K-Nearest Neighbors dan Decision Tree untuk Prediksi Kemenangan di MPL Season 13 Mobile Legend,” Jurnal Mahasiswa Teknik Informatika, vol. 8, no. 4, pp. 7553–7560, 2024, doi: 10.36040/jati.v8i4.10343.

M. N. Muttaqin and I. Kharisudin, “Analisis Sentimen pada Ulasan Aplikasi Gojek menggunakan Metode Support Vector Machine dan K Nearest Neighbor,” UNNES Journal of Mathematics, vol. 10, no. 2, pp. 22–27, 2021, doi: 10.15294/ujm.v10i2.48474.

M. Mardiana, “Sentimen Analisis terhadap Aplikasi Alfagift pada Google Play Store dengan Algoritma Support Vector Machine (SVM),” Palembang, 2023.

L. A. Fudholi, N. Rahaningsih, and R. D. Dana, “Sentimen Analisis Perilaku Penggemar Coldplay di Media Sosial Twitter menggunakan Metode Naive Bayes,” Jurnal Mahasiswa Teknik Informatika, vol. 8, no. 3, pp. 4150–4159, 2024, doi: 10.36040/jati.v8i3.9827.

R. A. Saputra, D. P. Ray, and F. Irwiensyah, “Analisis Sentimen Aplikasi Tokocrypto berdasarkan Ulasan pada Google Play Store menggunakan Metode Naïve Bayes,” KLIK: Kajian Ilmiah Informatika dan Komputer, vol. 4, no. 4, pp. 2028–2036, 2024, doi: 10.30865/klik.v4i4.1707.

L. Purnama and T. Wahyudi, “Analisa Sentimen tentang Piala Dunia U-20 Indonesia menggunakan Algoritma K-Nearest Neighbor,” Jurnal Sains dan Teknologi, vol. 6, no. 2, pp. 217–222, 2024, doi: 10.55338/saintek.v6i2.1397.

T. Ridwansyah, “Implementasi Text Mining terhadap Analisis Sentimen Masyarakat Dunia di Twitter terhadap Kota Medan menggunakan K-Fold Cross Validation dan Naïve Bayes Classifier,” KLIK: Kajian Ilmiah Informatika dan Komputer, vol. 2, no. 5, pp. 178–185, 2022, doi: 10.30865/klik.v2i5.362.

R. K. Dewi, Q. J. Adrian, H. Sulistiani, and F. Isnaini, “Dashboard Interaktif untuk Sistem Informasi Keuangan pada Pondok Pesantren Mazroatul’ulum,” Jurnal Teknologi dan Sistem Informasi (JTSI), vol. 2, no. 2, pp. 116–121, 2021, doi: 10.33365/jtsi.v2i2.883.

S. Navisa, L. Hakim, and A. Nabilah, “Komparasi Algoritma Klasifikasi Genre Musik pada Spotify menggunakan CRISP-DM,” Jurnal Sistem Cerdas, vol. 4, no. 2, pp. 114–125, 2021, doi: 10.37396/jsc.v4i2.162.

A. S. Muliana, D. Lestarini, and S. P. Raflesia, “Analysis of Public Sentiment on Election Results using Naïve Bayes in Social Media X,” Sistemasi: Jurnal Sistem Informasi, vol. 13, no. 6, pp. 2467–2478, 2024, doi: 10.32520/stmsi.v13i6.4592.

E. Constancio and K. Tania, “Penerapan Metode Supervised Learning dan Teknik Resampling untuk Prediksi Penipuan Transaksi Keuangan,” Building of Informatics, Technology and Science (BITS), vol. 6, no. 3, pp. 1427–1439, Dec. 2024, doi: 10.47065/bits.v6i3.6110.

M. Zahirma and R. Rumini, “Sentiment Analysis of Sunscreen Product Reviews using Naive Bayes Classifier Algorithm,” Sistemasi: Jurnal Sistem Informasi, vol. 14, no. 1, pp. 66–75, 2025.




DOI: https://doi.org/10.32520/stmsi.v14i2.5064

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