Sentiment Analysis of Neobank Digital Banking using Support Vector Machine Algorithm in Indonesia
DOI: http://dx.doi.org/10.30630/joiv.7.2.1652
Abstract
Currently, in the industrial era 4.0, information and communication technology is very developed, whereas, in this era, there is an increase in complex activities, one of which is in the banking sector. With the ease and efficiency of online finance, people want to switch to using digital banks. Neobank is an online savings and deposit application from Bank Neo Commerce (BCN) that the public can use by using the Internet. One of the online services is mobile banking which can be used by both Android and iOS versions of customers. Users can review Neobank's performance and services through the Google Play Store to improve and evaluate Neobank's performance. Neobank application reviews on the Google Play Store are increasing. Therefore, a review analysis is needed by conducting a sentiment analysis on Neobank's review. The data amounted to 3159 user reviews collected from reviews of the Neobank application on the Google Play Store. This study aims to classify Neobank user review data, including positive or negative sentiments. The method used in this study is an experimental method using the Support Vector Machine algorithm. The accuracy results obtained using the Support Vector Machine algorithm are 82.33%, which is owned by the scenario of 90% training data and 10% test data. The precision results are 82%, and recall is 81%. Future studies can add datasets from various sources so that there are even more datasets so as to increase the accuracy of model classification.
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H. Luo, L. Lin, K. Chen, M. F. Antwi-Afari, and L. Chen, “Digital technology for quality management in construction: A review and future research directions,†Dev. Built Environ., vol. 12, p. 100087, Dec. 2022, doi: 10.1016/J.DIBE.2022.100087.
J. Wang, X. Ma, J. Zhang, and X. Zhao, “Impacts of digital technology on energy sustainability: China case study,†Appl. Energy, vol. 323, p. 119329, Oct. 2022, doi: 10.1016/J.APENERGY.2022.119329.
J. C. Kouladoum, M. A. K. Wirajing, and T. N. Nchofoung, “Digital technologies and financial inclusion in Sub-Saharan Africa,†Telecomm. Policy, p. 102387, Jun. 2022, doi: 10.1016/J.TELPOL.2022.102387.
M. Iranmanesh, M. Ghobakhloo, M. Nilashi, M. L. Tseng, E. Yadegaridehkordi, and N. Leung, “Applications of disruptive digital technologies in hotel industry: A systematic review,†Int. J. Hosp. Manag., vol. 107, p. 103304, Oct. 2022, doi: 10.1016/J.IJHM.2022.103304.
Asosiasi Penyelenggara Jasa Internet Indonesia, “Laporan Survei Internet APJII 2019 – 2020,†Asos. Penyelenggara Jasa Internet Indonesia, 2020. https://apjii.or.id/survei.
W. A. Alkhowaiter, “Digital payment and banking adoption research in Gulf countries: A systematic literature review,†Int. J. Inf. Manage., vol. 53, p. 102102, Aug. 2020, doi: 10.1016/J.IJINFOMGT.2020.102102.
H. A. Alnemer, “Determinants of digital banking adoption in the Kingdom of Saudi Arabia: A technology acceptance model approach,†Digit. Bus., vol. 2, no. 2, p. 100037, Jan. 2022, doi: 10.1016/J.DIGBUS.2022.100037.
N. A. Windasari, N. Kusumawati, N. Larasati, and R. P. Amelia, “Digital-only banking experience: Insights from gen Y and gen Z,†J. Innov. Knowl., vol. 7, no. 2, p. 100170, Apr. 2022, doi: 10.1016/J.JIK.2022.100170.
F. Ernawan, A. Aminuddin, D. Nincarean, M. F. A. Razak, and A. Firdaus, “Three Layer Authentications with a Spiral Block Mapping to Prove Authenticity in Medical Images,†Int. J. Adv. Comput. Sci. Appl., vol. 13, no. 4, pp. 211–223, 2022, doi: 10.14569/IJACSA.2022.0130425.
L. V. Gorodianska, T. Nosenko, and V. Vember, “Neobanks operations and security features,†2019 IEEE Int. Sci. Conf. Probl. Infocommunications Sci. Technol. PIC S T 2019 - Proc., pp. 839–842, Oct. 2019, doi: 10.1109/PICST47496.2019.9061268.
T. Sharma and D. Rattan, “Malicious application detection in android — A systematic literature review,†Comput. Sci. Rev., vol. 40, p. 100373, May 2021, doi: 10.1016/J.COSREV.2021.100373.
J. Pang and J. Bian, “Android malware detection based on naive bayes,†Proc. IEEE Int. Conf. Softw. Eng. Serv. Sci. ICSESS, vol. 2019-Octob, pp. 483–486, 2019, doi: 10.1109/ICSESS47205.2019.9040796.
P. K. Tiwari, S. R. Basireddy, and T. Velayutham, “Identification of Possibly Intemperate Permission Demands in Android Apps,†Proc. 2nd Int. Conf. Innov. Pract. Technol. Manag. ICIPTM 2022, pp. 101–106, 2022, doi: 10.1109/ICIPTM54933.2022.9753830.
A. Aminuddin, “Android Assets Protection Using RSA and AES Cryptography to Prevent App Piracy,†2020 3rd Int. Conf. Inf. Commun. Technol. ICOIACT 2020, pp. 461–465, 2020, doi: 10.1109/ICOIACT50329.2020.9331988.
M. Rahardi, A. Aminuddin, F. F. Abdulloh, and R. A. Nugroho, “Sentiment Analysis of Covid-19 Vaccination using Support Vector Machine in Indonesia,†Int. J. Adv. Comput. Sci. Appl., vol. 13, no. 6, p. 2022, 2022, doi: 10.14569/IJACSA.2022.0130665.
S. Zahoor and R. Rohilla, “Twitter Sentiment Analysis Using Machine Learning Algorithms: A Case Study,†Proc. - 2020 Int. Conf. Adv. Comput. Commun. Mater. ICACCM 2020, pp. 194–199, 2020, doi: 10.1109/ICACCM50413.2020.9213011.
E. Y. Sari, A. D. Wierfi, and A. Setyanto, “Sentiment Analysis of Customer Satisfaction on Transportation Network Company Using Naive Bayes Classifier,†2019 Int. Conf. Comput. Eng. Network, Intell. Multimedia, CENIM 2019 - Proceeding, vol. 2019-November, Nov. 2019, doi: 10.1109/CENIM48368.2019.8973262.
S. Ranjan and S. Mishra, “Comparative Sentiment Analysis of App Reviews,†2020 11th Int. Conf. Comput. Commun. Netw. Technol. ICCCNT 2020, Jul. 2020, doi: 10.1109/ICCCNT49239.2020.9225348.
H. Mehyar, M. Saeed, H. B. A. Al-Ja’afreh, and R. Al-Adaileh, “The impact of electronic word of mouth on consumers purchasing intention,†J. Theor. Appl. Inf. Technol., vol. 98, no. 2, pp. 183–193, 2020.
N. Colmekcioglu, R. Marvi, P. Foroudi, and F. Okumus, “Generation, susceptibility, and response regarding negativity: An in-depth analysis on negative online reviews,†J. Bus. Res., vol. 153, pp. 235–250, Dec. 2022, doi: 10.1016/J.JBUSRES.2022.08.033.
J. Qin and M. Zeng, “An integrated method for product ranking through online reviews based on evidential reasoning theory and stochastic dominance,†Inf. Sci. (Ny)., vol. 612, pp. 37–61, Oct. 2022, doi: 10.1016/J.INS.2022.08.070.
A. Yaqin, M. Rahardi, and F. F. Abdulloh, “Accuracy Enhancement of Prediction Method using SMOTE for Early Prediction Student’s Graduation in XYZ University,†Int. J. Adv. Comput. Sci. Appl., vol. 13, no. 6, pp. 418–424, 2022.
M. Rahardi, F. F. Abdulloh, and W. S. Putra, “A Blind Robust Image Watermarking on Selected DCT Coefficients for Copyright Protection,†Int. J. Adv. Comput. Sci. Appl., vol. 13, no. 7, pp. 719–726, 2022.
H. Hairani, M. Innuddin, and M. Rahardi, “Accuracy Enhancement of Correlated Naive Bayes Method by Using Correlation Feature Selection (CFS) for Health Data Classification,†2020 3rd Int. Conf. Inf. Commun. Technol. ICOIACT 2020, pp. 51–55, 2020, doi: 10.1109/ICOIACT50329.2020.9332021.
P. P. Gokul, B. K. Akhil, and K. K. M. Shiva, “Sentence similarity detection in Malayalam language using cosine similarity,†RTEICT 2017 - 2nd IEEE Int. Conf. Recent Trends Electron. Inf. Commun. Technol. Proc., vol. 2018-January, pp. 221–225, Jul. 2017, doi: 10.1109/RTEICT.2017.8256590.
F. F. Abdulloh, M. Rahardi, A. Aminuddin, S. D. Anggita, and A. Y. A. Nugraha, “Observation of Imbalance Tracer Study Data for Graduates Employability Prediction in Indonesia,†Int. J. Adv. Comput. Sci. Appl., vol. 13, no. 8, pp. 169–174, 2022.
A. C. Emcha, Widyawan, and T. B. Adji, “Quotation extraction from Indonesian online news,†2019 Int. Conf. Inf. Commun. Technol. ICOIACT 2019, pp. 408–412, 2019, doi: 10.1109/ICOIACT46704.2019.8938558.
Z. Mustaffa, M. H. Sulaiman, F. Ernawan, Y. Yusof, and M. F. M. Mohsin, “Dengue outbreak prediction: Hybrid meta-heuristic model,†Proc. - 2018 IEEE/ACIS 19th Int. Conf. Softw. Eng. Artif. Intell. Netw. Parallel/Distributed Comput. SNPD 2018, pp. 271–274, Aug. 2018, doi: 10.1109/SNPD.2018.8441095.
T. M. Ma, K. Yamamori, and A. Thida, “A Comparative Approach to Naïve Bayes Classifier and Support Vector Machine for Email Spam Classification,†2020 IEEE 9th Glob. Conf. Consum. Electron. GCCE 2020, pp. 324–326, 2020, doi: 10.1109/GCCE50665.2020.9291921.
A. Aminuddin and F. Ernawan, “AuSR1: Authentication and self-recovery using a new image inpainting technique with LSB shifting in fragile image watermarking,†J. King Saud Univ. - Comput. Inf. Sci., Feb. 2022, doi: 10.1016/J.JKSUCI.2022.02.009.
A. Aminuddin and F. Ernawan, “AuSR2: Image watermarking technique for authentication and self-recovery with image texture preservation,†Comput. Electr. Eng., vol. 102, no. April, p. 108207, 2022, doi: 10.1016/J.COMPELECENG.2022.108207.
R. M. Amir Latif, M. Talha Abdullah, S. U. Aslam Shah, M. Farhan, F. Ijaz, and A. Karim, “Data scraping from google play store and visualization of its content for analytics,†2019 2nd Int. Conf. Comput. Math. Eng. Technol. iCoMET 2019, pp. 1–8, 2019, doi: 10.1109/ICOMET.2019.8673523.
K. Kusnawi and A. Hendra Wijaya, “Sentiment Analysis of Pancasila Values in Social Media Life Using the Naive Bayes Algorithm,†Proc. - 2021 Int. Semin. Appl. Technol. Inf. Commun. IT Oppor. Creat. Digit. Innov. Commun. within Glob. Pandemic, iSemantic 2021, pp. 96–101, Sep. 2021, doi: 10.1109/ISEMANTIC52711.2021.9573194.