Penggunaan Seleksi Fitur Query Expansion Ranking dan Genetic Algorithm-Support Vector Machine untuk Analisis Sentimen pada Aplikasi Perbankan Jenius
DOI:
https://doi.org/10.20956/ejsa.v7i1.47395Keywords:
Genetic Algorithm, Jenius, Query Expansion Ranking, SVM, GA-SVM, QER-SVMAbstract
Jenius is a digital banking product from BTPN launched in 2016. In 2021, various opinions emerged on Twitter regarding cases of lost customer funds, necessitating sentiment analysis to understand public perception. This study used the Support Vector Machine (SVM) method with two feature selection approaches: Query Expansion Ranking (QER) and Genetic Algorithm (GA). The data used were 2,008 manually labeled tweets. The results showed that the QER-SVM method produced an accuracy of 87.81%, while the GA-SVM achieved an accuracy of 88.31% with improvements in precision and F-measure. Thus, the combination of Genetic Algorithm and SVM was more effective in classifying sentiment towards the Jenius application on Twitter.
References
Fikri, M. I., Sabrila, T. S., & Azhar, Y. Perbandingan Metode Naïve Bayes dan Support Vector Machine pada Analisis Sentimen Twitter. SMATIKA Jurnal, 10(2), 71–76, 2020. https://doi.org/10.32664/smatika.v10i02.455.
Haddi, E., Liu, X., & Shi, Y. The Role of Text Pre-processing in Sentiment Analysis. Procedia Computer Science, 17, 26–32, 2013. https://doi.org/10.1016/j.procs.2013.05.005.
Ernawati, S., Yulia, E. R., Frieyadie, & Samudi. Implementation of The Naïve Bayes Algorithm with Feature Selection using Genetic Algorithm for Sentiment Review Analysis of Fashion Online Companies. Proceedings of the 6th International Conference on Cyber and IT Service Management (CITSM), 1–5, 2018. https://doi.org/10.1109/CITSM.2018.8674286.
Zainuddin, N., & Selamat, A. Sentiment Analysis using Support Vector Machine. Proceedings of the International Conference on Computer, Communications, and Control Technology (I4CT), 333–337, 2014. https://doi.org/10.1109/I4CT.2014.6914200.
Mentari, N. D., Fauzi, M. A., & Muflikhah, L. Analisis Sentimen Kurikulum 2013 pada Sosial Media Twitter Menggunakan Metode K-Nearest Neighbor dan Feature Selection Query Expansion Ranking. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2(8), 2739–2743, 2018.
Mulyani, S., Thamrin, S. A., & Siswanto, S. Analisis Sentimen Masyarakat pada Kebijakan Vaksinasi Covid-19 di Twitter Menggunakan Metode Support Vector Machine dengan Kernel Radial Basis Function Berbasis Fitur Leksikon. Jambura Journal of Probability and Statistics, 3(2), 110–119, 2022. https://doi.org/10.34312/jjps.v3i2.16663
Rezki, N., Thamrin, S. A., & Siswanto, S. Sentiment Analysis of Merdeka Belajar Kampus Merdeka Policy Using Support Vector Machine with Word2Vec. BAREKENG: Jurnal Ilmu Matematika dan Terapan, 17(1), 481–486, 2023. https://doi.org/10.30598/barekengvol17iss1pp0481-0486
Dadgar, S. M. H., Araghi, M. S., & Farahani, M. M. A Novel Text Mining Approach Based on TF-IDF and Support Vector Machine for News Classification. Proceedings of the IEEE International Conference on Engineering and Technology (ICETECH), 112–116, 2016. https://doi.org/10.1109/ICETECH.2016.7569223
Fanissa, S., Fauzi, M. A., & Adinugroho, S. Analisis Sentimen Pariwisata di Kota Malang Menggunakan Metode Naive Bayes dan Seleksi Fitur Query Expansion Ranking. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2(8), 2766–2770, 2018.
Ladha, L., & Deepa, T. Feature Selection Methods and Algorithms. International Journal on Computer Science and Engineering, 3(5), 1787–1797, 2011.
Thiang, Kurniawan, R., & Ferdinando, H. Implementasi Algoritma Genetika pada Mikrokontroler MCS51 untuk Mencari Rute Terpendek. Proceedings of Seminar of Intelligent Technology and Its Applications (SITIA), Institut Teknologi Sepuluh Nopember, 2001.
Novantirani, A., Sabariah, M. K., & Effendy, V. Analisis Sentimen pada Twitter Mengenai Penggunaan Transportasi Umum Darat dalam Kota dengan Metode Support Vector Machine. e-Proceeding of Engineering, 2(1), 1177–1183, 2015.
Hsu, S. Developing a Scale for Teacher Integration of Information and Communication Technology in Grades 1–9. Journal of Computer Assisted Learning, 26(3), 175–189, 2010. https://doi.org/10.1111/j.1365-2729.2010.00348.x
Prasetyo, E. Data Mining: Konsep dan Aplikasi Menggunakan MATLAB. Yogyakarta: CV Andi Offset, 2012.
Fadil, M., Islamiyati, A., & Thamrin, S. A. Classification of Nutritional Status in Toddlers using the Support Vector Machine Method. Communications in Mathematical Biology and Neuroscience, 2025, 2025. https://doi.org/10.28919/cmbn/9126
Downloads
Published
Issue
Section
License
Copyright
It is the author's responsibility to ensure that his or her submitted work does not infringe any existing copyright. Authors should obtain permission to reproduce or adapt copyrighted material and provide evidence of approval upon submitting the final version of a manuscript.
