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Article Content:-
Abstract
This research aims to evaluate the quality of the Livin by Mandiri mobile banking application based on user reviews from the Google Play Store. A Support Vector Machine (SVM) classifier is applied to distinguish between positive and negative sentiments. Modified Particle Swarm Optimization (MPSO) is used for parameter tuning because SVM performance is sensitive to parameter selection. The baseline SVM attains 90.30% accuracy, 90.64% precision, 89.86% recall, and 90.25% F1-score with a linear kernel with C = 1 and a 90:10 training-testing split. Accuracy is increased to 91.33% by the SVM-MPSO model, with precision of 90.87%, recall of 91.71%, and F1-Score of 91.29%. The Streamlit framework for interactive sentiment classification is used to deploy the model.
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References
Indonesian Internet Service Providers Association (APJII), “Indonesia’s internet users reach 221 million people,” APJII, [Online]. Available:
https://apjii.or.id/berita/d/apjiijumlahpengguna-internet-indonesia-tembus-221-juta-orang. [Accessed: Oct. 5, 2024].
Financial Services Authority of Indonesia (OJK), “Indonesian banking statistics,” [Online]. Available:
https://www.ojk.go.id/id/kanal/perbankan/data-dan-statistik/statistik-perbankan-indonesia/default.aspx. [Accessed: Oct. 7, 2024].
C. Z. V. Junus, Tarno, and P. Kartikasari, “Classification using Support Vector Machine and Random Forest for early detection of diabetes mellitus risk,” J. Gaussian, vol. 11. no. 3, pp. 386–396, 2022. [Online]. Available:
https://doi.org/10.14710/j.gauss.11.3.386-396
Bank Mandiri, “Bank Mandiri profile: Livin’ by Mandiri category,” Bank Mandiri, [Online]. Available: https://www.bankmandiri.co.id. [Accessed: Oct. 6, 2024].
Z. I. Alfianti, “Sentiment analysis of cosmetic reviews on the Femaledaily website using Naive Bayes and Support Vector Machine based on Particle Swarm Optimization,” Master’s thesis, Computer Science Program, STMIK Nusa Mandiri, Jakarta, 2019.
W. Astriningsih, “Multi-aspect identification and sentiment analysis on hotel reviews using Deep Learning,” Master’s thesis, Informatics Program, Universitas Islam Indonesia, Yogyakarta, 2023.
U. I. Larasati, M. A. Muslim, R. Arifudin, and Alamsyah, “Improving the accuracy of Support Vector Machine using Chi-Square statistic and TF-IDF for movie review sentiment analysis,” Sci. J. Inform., vol. 6, no. 1. pp. 138–149, 2019. [Online]. Available: http://journal.unnes.ac.id/nju/index.php/sji
G. H. A. Noer, “Implementation of Naïve Bayes algorithm and TF-IDF in sentiment analysis of review data (Case study: E-commerce application Shopee reviews on Google Playstore),” Bachelor’s thesis, Informatics Program, UIN Syarif Hidayatullah, Jakarta, 2023.
G. Salton and C. Buckley, “Term-weighting approaches in automatic text retrieval,” Inf. Process. Manag., vol. 24, no. 5, pp. 513–523, 1988. [Online]. Available:
https://doi.org/10.1016/0306-4573(88)90021-0
C. Cortes, V. Vapnik, and L. Saitta, “Support-vector networks,” Mach. Learn., vol. 20. no. 3, pp. 273–297, 1995.
S. K. Shevade, S. S. Keerthi, S. Bhattacharyya, and K. R. K. Murthy, “Improvements to the SMO algorithm for SVM regression,” IEEE Trans. Neural Netw., vol. 11. no. 5, pp. 1188–1193, 2000. [Online]. Available: https://doi.org/10.1109/72.870050
O. A. M. López, A. M. López, and J. Crossa, Multivariate Statistical Machine Learning Methods for Genomic Prediction. Springer Nature Switzerland, 2022. [Online]. Available:
https://doi.org/10.1007/978-3-030-89010-0
S. R. Munthe, “Application of Kernel Support Vector Machine (SVM) method for sentiment classification of Shopee Food on Twitter,” Bachelor’s thesis, Statistics Program, Universitas Diponegoro, Semarang, 2024.
Y. Shi and R. Eberhart, “A modified particle swarm optimizer,” in Proc. IEEE Int. Conf. Evolutionary Computation, pp. 69–73, 1998. [Online]. Available: https://doi.org/10.1109/ICEC.1998.699146
R. Al Ghivary, N. Wulandari, N. Srikandi, and F. A. M. Nazilatul, “The role of data visualization in supporting population data analysis in Indonesia,” PENTAHELIX J. Public Admin., vol. 1. no. 1. pp. 57–62, 2023.
A. Jalil, A. Homaidi, and Z. Fatah, “Implementation of Support Vector Machine algorithm for classifying stunting status in toddlers,” G-Tech: J. Appl. Technol., vol. 8, no. 3, pp. 2070–2079, 2024. [Online]. Available: https://doi.org/10.33379/gtech.v8i3.481
S. A. Puri, “Chronic kidney failure prediction using Support Vector Machine algorithm based on Particle Swarm Optimization (Case study: RS Roemani Muhammadiyah, Semarang),” Bachelor’s thesis, Statistics Program, Universitas Diponegoro, Semarang, 2023.