Analisis Sentimen E-Wallet Menggunakan Support Vector Machine Berbasis Particle Swarm Optimization
- Vina Vamilina Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
-
Rice Novita
*
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru,
Indonesia
(*) Corresponding Author
Abstract
E-Wallet applications in Indonesia have started to be in demand since the Covid-19 pandemic. The object being analyzed is an e-wallet application that is widely used in Indonesia and can be downloaded on the Google Playstore. The applications analyzed are Dana, Ovo, PayPal Link Aja and Doku. The advantages of these five applications are that Dana is user friendly or easy to use, while using Ovo is superior in terms of benefits, and Doku is superior in terms of security, Link Aja tends to be perceived by consumers in a neutral condition between security and user convenience because it is an e-wallet. It is still considered new in Indonesia, and PayPal has become a successful online payment system in C2C field. The focus of this research is to compare the comments of the users of the five applications. The method used in this study is the Support Vector Machine (SVM) algorithm. To produce high accuracy it is optimized using the Particle Swarm Optimization (PSO) algorithm. This was taken based on previous studies which stated that SVM-PSO has the highest percentage of accuracy compared to other algorithms. The data used is a thousand (1000) per application. So, the total amount of data is five thousand (5000) data. The results of the research show that the Ovo e-wallet is superior because it has the most positive comments, namely 579 and 421 negative comments, while the lowest position is occupied by Link Aja which only has 579 positive comments and 421 negatuve comments. In the process of sentiment analysis, the accuracy percentage of the SVM-PSO algorithm was also obtained, which was 91.10% in the Link Aja application. This means that SVM-PSO is very suitable to be combined to get the highest accuracy
Downloads
References
Lei, D., & Slocum, J. (2020). C hapter 1 C rafting a S ustainable B usiness S trategy. Demystiying Your Business Strategy, pp.13-26. https://doi.org/10.4324/978023109007-5.
Johnson, V. L., Kiser, A., Washington, R., & Torres, R (2018). Limitations to the rapid adoption of M-Payment services: Understanding the impact of privacy risk on M-Payment services. Computers in Human Behavior, vol.79, pp.111-112. https://doi.org/10.1016/j.cbh.2017.10.035.
A. Mustofa and R. Novita, “Klasifikasi Sentimen Masyarakat Terhadap Pemberlakuan Pembatasan Kegiatan Masyarakat Menggunakan Text Mining Pada Twitter,” Build. Informatics, Technol. Sci. (BITS, vol. 4, no. 1, pp. 200–208, 2022, doi: 10.47065/bits.v4i1.1628.
A. K. Negara, S. A. Immawati, P. S. Manajemen, U. Muhammadiyah, and J. S. Manajemen, “Pertimbangan dan Sikap Milenial terhadap Minat Menggunakan E-Wallet : Pada Masa PSBB Pandemi Covid-19 di Kota Tangerang,” ORGANUM J. saintifik Manaj. dan Akunt., vol. 03, no. 02, pp. 64–81, 2020, doi: Saintifik Manajemen dan Akuntanhttps://doi.org/10.35138/organu m.v3i2.104.
Kaur, P., Dhir, A., Bodhi, R., Singh, T., & Almotairi, M. (2020). Why do people use and recommend m-wallets? Journal of Retailing and Consumer Services. vol.56, no.102091. https://doi.org/10.1016/j.jretconser.2020.102091.
Aji, H.M., Berakon, I., & Md Husin, M. (2020), COVID-19 and e-wallet usage intention: A multigroup analysis between Indonesia and Malaysia. Cogent Business and Maanagemen,7(1). https://doi.org/10.1080/23311975.2020.1804181.
Kakhkharov, J., & Bianchi, R. J. (2022). COVID-19 and policy responses: Early evidence in banks and FinTech Stocks. Pacific Basin Finance Journal, Vol.74. no.101815. https://doi.org/10.1016/j.jretconser.2020.102091.
Shaury, S. (2019). Analisis Positioning E-Wallet Berdasarkan Perceptual Mapping Positioning Analysis of E-Wallet Based on Perceptual Mapping. Vol.3, no.1, pp.73-82.
Fauzi, Agung (2022). Preferensi Produk E-Wallet Di Indonesia (Studi Pada: Go-Pay, Ovo, Dana, Dan Link Aja!) 1. NUSANTARA : Jurnal Ilmu Pengetahuan Sosial, vol.9, no.5, pp.1571–1579. https://doi.org/10.31604/jips.v9i4.2022.1571-1579
F. Mumpuni, dkk. (2022) “Perbandingan Metode Klasifikasi Sentimen Analisis Penggunaan E-Wallet Menggunakan Algoritma Naïve Bayes dan K-Nearest Neighbor,” METIK, vol. 6, no. 2, pp. 97–103, doi: 10.47002/metik.v6i2.372.
S. A. Sutresno. 2023 “Analisis Sentimen Masyarakat Indonesia Terhadap Dampak Penurunan Global Sebagai Akibat Resesi di Twitter”. Build. Informatics, Technol. Science (BITS), vol. 4, no. 4, pp. 1959–1966. https://doi: 10.47065/bits.v4i4.3149.
E. F. Saraswita, D. P. Rini, and A. Abdiansah, “Analisis Sentimen E-Wallet di Twitter Menggunakan Support Vector Machine dan Recursive Feature Elimination,” J. Media Inform. Budidarma, vol. 5, no. 4, p. 1195, 2021, doi: 10.30865/mib.v5i4.3118.
Zhou, W., Chen, M., Yang, Z., & Song, X. (2020). Socio-Economic Planning Science Real estate risk measurement and early warning based on PSO-SVM. Scio-Economic Planning Sciences, 101001. https://doi.org/10.1016/j.seps.2020.101001.
Huang, W., Liu, H., Zhang, Y., Mi, R., Tong. C., Xiao, W., &Shuai, B. (2021). Railway dangerous good transportation system risk identification : Applied Soft Computing, vol.109, no.107541. https://doi.org/10.1016/j.asoc.2021.107541.
Liu , W., Guo, G., Chen, F., & Chen, Y. (2019). Meteological pattern analysis assisted daily PM2.5 grades prediction using SVM optimized by PSO algorithm, Atmospheric Pollution Research, vol.10, no.5, pp. 1482-1491. https:doi.org/10.1016/j.apr.2019.04.005
Chen, S., Wang, J., & Zhang, H.(2019). Technological Forecasting & Social Change A hbrid PSO-SVM model based on clustering algorithm for short-term atmospheric pollutant concentration forecasting. Technological Forecasting Social Change, vol. 1496, pp.41-54. https://doi.org/10.1016/j.techfore.2019.05.015.
Deng, C., Li, H., Peng, D., Liu., Zhu, Q., & Li, C. (2021). Modelling the coupling evolution of the water environment and social economic system using PSO-SVM in the Yangtze River Economic. Ecological Indicators, vol.129, no.108012. https://doi.org/10.1016/j.ecolind,2021.108012.
F. A. Wenando, R. Hayami, Bakaruddin, and A. Y. Novermahakim, “Tweet Sentiment Analysis for 2019 Indonesia Presidential Election Results using Various Classification Algorithms,” Proceeding-1st Int. Conf. Inf. Technol. Adv. Mech. Electr. Eng.ICITAMEE 2020, pp. 279–282, 2020, doi: 10.1109/ICITAMEE50454.2020.9398513.
F. S. Jumeilah, “Penerapan Support Vector Machine (SVM) untuk Pengkategorian Penelitian,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 1, no. 1, pp. 19–25, 2017, doi: 10.29207/resti.v1i1.11.
Vapnik, V., Golowich, S.E., Ave, M., & Hill, M. (2020). Support Vector Method for Function Approximation, Regression Estimation, and Signal Processing. Journal of Computer and Communications, vol.8, no.11, pp.281-287.
W. S. Dharmawan, “Komparasi Algoritma Klasifikasi Svm-Pso Dan C4.5-Pso Dalam Prediksi Penyakit Jantung,” I N F O R M A T I K A, vol. 13, no. 2, p. 31, 2022, doi: 10.36723/juri.v13i2.301.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Analisis Sentimen E-Wallet Menggunakan Support Vector Machine Berbasis Particle Swarm Optimization
Pages: 40−48
Copyright (c) 2023 Vina Vamilina, Rice Novita
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
