ANALISIS SENTIMEN ULASAN PENGGUNA BSI MOBILE PADA GOOGLE PLAY DENGAN PENDEKATAN SUPERVISED LEARNING
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Singh, N., & Sinha, N. (2018). A Study on Mobile Banking and its Impact on Customer’s Banking Transactions: A Comparative Analysis of Public and Private Sector Banks in India. https://doi.org/10.1177/2455265820160207, 5(2), 57–70. SAGE PublicationsSage India: New Delhi, India.
Singla, Z., Randhawa, S., & Jain, S. (2018). Sentiment analysis of customer product reviews using machine learning. Proceedings of 2017 International Conference on Intelligent Computing and Control, I2C2 2017, 2018-January, 1–5. Institute of Electrical and Electronics Engineers Inc.
Wiratama, G. P., & Rusli, A. (2019). Sentiment analysis of application user feedback in Bahasa Indonesia using multinomial naive bayes. Proceedings of 2019 5th International Conference on New Media Studies, CONMEDIA 2019, 223–227. Institute of Electrical and Electronics Engineers Inc.
Arifiyanti, A. A., & Wahyuni, E. D. (2020). Emoji and emoticon in tweet sentiment classification. Proceeding - 6th Information Technology International Seminar, ITIS 2020, 145–150. Institute of Electrical and Electronics Engineers Inc.
Bayhaqy, A., Sfenrianto, S., Nainggolan, K., & Kaburuan, E. R. (2018). Sentiment Analysis about E-Commerce from Tweets Using Decision Tree, K-Nearest Neighbor, and Naïve Bayes. 2018 International Conference on Orange Technologies, ICOT 2018. Institute of Electrical and Electronics Engineers Inc.
datareportal.com. (2022, February 15). Digital 2022: Indonesia. Retrieved May 22, 2022, from https://datareportal.com/reports/digital-2022-indonesia
Gorunescu, F. (2011). Data mining: Concepts, models and techniques. Intelligent Systems Reference Library, 12.
Haidar, A., As-Salafiyah, A., & Herindar, E. (2022). Sentiment Analysis of Digital Sharia Banking. Ekonomi Islam Indonesia, 4(1), 6694–6702.
Laukkanen, T. (2017). Mobile banking. International Journal of Bank Marketing, 35(7), 1042–1043. Emerald Publishing Limited.
Ruslim, K. I., Adikara, P. P., & Indriati. (2019). Analisis Sentimen Pada Ulasan Aplikasi Mobile Banking Menggunakan Metode Support Vector Machine dan Lexicon Based Features . Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 3(7).
Samal, B. R., Behera, A. K., & Panda, M. (2017). Performance analysis of supervised machine learning techniques for sentiment analysis. Proceedings of 2017 3rd IEEE International Conference on Sensing, Signal Processing and Security, ICSSS 2017, 128–133. Institute of Electrical and Electronics Engineers Inc.
Schweer, D., & Sahl, J. C. (2017). The Digital Transformation of Industry – The Benefit for Germany, 23–31. Springer, Cham.
Shaikh, A. A., & Karjaluoto, H. (2015). Mobile banking adoption: A literature review. Telematics and Informatics, 32(1), 129–142. Pergamon.
Singh, N., & Sinha, N. (2018). A Study on Mobile Banking and its Impact on Customer’s Banking Transactions: A Comparative Analysis of Public and Private Sector Banks in India. https://doi.org/10.1177/2455265820160207, 5(2), 57–70. SAGE PublicationsSage India: New Delhi, India.
Singla, Z., Randhawa, S., & Jain, S. (2018). Sentiment analysis of customer product reviews using machine learning. Proceedings of 2017 International Conference on Intelligent Computing and Control, I2C2 2017, 2018-January, 1–5. Institute of Electrical and Electronics Engineers Inc.
Wiratama, G. P., & Rusli, A. (2019). Sentiment analysis of application user feedback in Bahasa Indonesia using multinomial naive bayes. Proceedings of 2019 5th International Conference on New Media Studies, CONMEDIA 2019, 223–227. Institute of Electrical and Electronics Engineers Inc.
How to Cite
[1]
A. A. Arifiyanti, N. R. Shantika, and A. O. Syafira, “ANALISIS SENTIMEN ULASAN PENGGUNA BSI MOBILE PADA GOOGLE PLAY DENGAN PENDEKATAN SUPERVISED LEARNING ”, JIP, vol. 9, no. 3, pp. 283-288, May 2023.
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Copyright (c) 2023 Amalia Anjani Arifiyanti, Nurisa Rahma Shantika, Anggy Oktaviana Syafira
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