How Positive Sentiment of Telemedicine Applications using Naïve Bayes and Support Vector Machine?
DOI:
https://doi.org/10.33379/gtech.v8i2.4046Keywords:
Fine-Grained Sentiment Analysis, Naïve Bayes, Support Vector Machine, TelemedicineAbstract
The telemedicine software lets users schedule appointments with physicians or hospitals, purchase medications online, read health-related news and publications, and have online consultations with doctors. Public opinion is necessary for this application to meet societal needs. The public's perception of contemporary health applications can be ascertained by looking to public opinion. Fine-grained sentiment analysis is used in public opinion taking to extract information from evaluations posted by the public and categorize it into positive and negative groups. Using RapidMiner as a supporting tool, sentiment analysis uses the Naïve Bayes Classification algorithm and Support Vector Machine to determine an accuracy value. After analyzing data from two different sources—Twitter and Playstore—it can be said that the Support Vector Machine algorithm performs 90.99% more accurately in predicting public opinion than the Naïve Bayes algorithm, which achieves 88.55% accuracy.
References
Andreadis, K., Muellers, K., Ancker, J. S., Horowitz, C., Kaushal, R., & Lin, J. J. (2023). Telemedicine Impact on the Patient–Provider Relationship in Primary Care During the COVID-19 Pandemic. Medical Care, 61(Suppl 1), S83–S88. https://doi.org/10.1097/MLR.0000000000001808
Annur, H. (2018). Klasifikasi Masyarakat Miskin Menggunakan Metode Naive Bayes. ILKOM Jurnal Ilmiah, 10(2), 160–165. https://doi.org/10.33096/ILKOM.V10I2.303.160-165
Bau, Y., & Tee, E. (2023). Sentiment Analysis of E-Wallet Companies: Exploring Customer Ratings and Perceptions. Shdl.Mmu.Edu.MyYT Bau, EL Tee, CL GohJournal of Logistics, Informatics and Service Science, 2023•shdl.Mmu.Edu.My. http://shdl.mmu.edu.my/id/eprint/11934
Brem, A., Viardot, E., & Nylund, P. A. (2021). Implications of the coronavirus (COVID-19) outbreak for innovation: Which technologies will improve our lives? Technological Forecasting and Social Change, 163, 120451. https://doi.org/10.1016/J.TECHFORE.2020.120451
Deviyanto, A., & Wahyudi, M. D. R. (2018). PENERAPAN ANALISIS SENTIMEN PADA PENGGUNA TWITTER MENGGUNAKAN METODE K-NEAREST NEIGHBOR. JISKA (Jurnal Informatika Sunan Kalijaga), 3(1), 1. https://doi.org/10.14421/JISKA.2018.31-01
Ferdiansyah, F., & Wella, W. (2022). Comparative Study of Hoax Detection using Support Vector Machine Algorithm, Naive Bayes, Random Forest and K-Nearest Neighbor. Ieeexplore.Ieee.OrgIF Ferdiansyah2022 13th International Conference on Information and, 2022•ieeexplore.Ieee.Org. https://ieeexplore.ieee.org/abstract/document/9952630/
Haleem, A., Javaid, M., Singh, R. P., & Suman, R. (2021). Telemedicine for healthcare: Capabilities, features, barriers, and applications. Sensors International, 2, 100117. https://doi.org/10.1016/j.sintl.2021.100117
He, W., Zhang, Z., management, W. L.-I. journal of information, & 2021, undefined. (2020). Information technology solutions, challenges, and suggestions for tackling the COVID-19 pandemic. Elsevier. https://www.sciencedirect.com/science/article/pii/S0268401220314869
Imarotunnisa Oktavia, D., Zain Hamid, F., Made Widhi, N. S., Niaga, A., Negeri Jakarta, P., GA Siwabessy, J. D., & Beji, K. (2020). Pengaruh Kredibilitas Perusahaan Terhadap Kepercayaan Konsumen Pada Penggunaan Aplikasi Kesehatan Halodoc. Proceeding.Isas.or.IdDI Oktavia, FZ HamidProsiding Seminar Nasional Terapan Riset Inovatif (SENTRINOV), 2020•proceeding.Isas.or.Id, 6(2). https://proceeding.isas.or.id/index.php/sentrinov/article/view/461
Kemp, S. (2021). Digital in Indonesia: All the Statistics You Need in 2021 — DataReportal – Global Digital Insights. https://datareportal.com/reports/digital-2021-indonesia
Kornelius, K., & Wella, W. (2021). Is the Internet in Indonesia Has a Good Sentiment from Netizen? Ieeexplore.Ieee.OrgKornelius, Wella2021 6th International Conference on New Media Studies (CONMEDIA), 2021•ieeexplore.Ieee.Org. https://ieeexplore.ieee.org/abstract/document/9616995/
Laili, M. (2016). Analisis implementasi sistem telemedika dan e-health di indonesia serta prospek kedepannya. https://www.academia.edu/download/56539040/Analisis_implementasi_system_telemedika_dan_e-health.pdf
Lee, P. S., Koo, S., & Panter, S. (2021). The Value of Physical Examination in the Era of Telemedicine. Journal of the Royal College of Physicians of Edinburgh, 51(1), 85–90. https://doi.org/10.4997/jrcpe.2021.122
Li, H. (2024). Support Vector Machine. Machine Learning Methods, 127–177. https://doi.org/10.1007/978-981-99-3917-6_7
Octaviani, P., Wilandari, Y., Gaussian, D. I.-J., & 2014, undefined. (2014). Penerapan Metode Klasifikasi Support Vector Machine (SVM) pada Data Akreditasi Sekolah Dasar (SD) di Kabupaten Magelang. Ejournal3.Undip.Ac.IdPA Octaviani, Y Wilandari, D IspriyantiJurnal Gaussian, 2014•ejournal3.Undip.Ac.Id, 3(4), 811–820. https://ejournal3.undip.ac.id/index.php/gaussian/article/view/8092
Rahman, M. F., Alamsah, D., Darmawidjadja, M. I., & Nurma, I. (2017). Klasifikasi Untuk Diagnosa Diabetes Menggunakan Metode Bayesian Regularization Neural Network (RBNN). Jurnal Informatika, 11(1), 36. https://doi.org/10.26555/JIFO.V11I1.A5452
Rofiqoh, U., Perdana, R., Teknologi, M. F.-J. P., & 2017, undefined. (2017). Analisis sentimen tingkat kepuasan pengguna penyedia layanan telekomunikasi seluler indonesia pada twitter dengan metode Support Vector Machine dan Lexicon. J-Ptiik.Ub.Ac.IdU Rofiqoh, RS Perdana, MA FauziJurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 2017•j-Ptiik.Ub.Ac.Id, 1(12), 1725–1732. http://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/628
Saleh, A. (2015). Implementasi metode klasifikasi Naive Bayes dalam memprediksi besarnya penggunaan listrik rumah tangga. Citec.Amikom.Ac.IdA SalehCreative Information Technology Journal, 2015•citec.Amikom.Ac.Id, 2(3). http://citec.amikom.ac.id/main/index.php/citec/article/view/49
Sari, F. (2019). Analisis sentimen pelanggan toko online Jd. Id menggunakan metode Naïve Bayes Classifier berbasis konversi ikon emosi. Jurnal.Umk.Ac.IdFV Sari, A WibowoSimetris: Jurnal Teknik Mesin, Elektro Dan Ilmu Komputer, 2019•jurnal.Umk.Ac.Id, 10(2). https://jurnal.umk.ac.id/index.php/simet/article/view/3487
Setiawan, G., Palit, H., Infra, E. S.-J., & 2019, undefined. (2019). Aspect Based Sentiment Analysis pada Layanan Umpan Balik Universitas dengan Menggunakan Metode Naïve Bayes dan Latent Semantic Analysis. Publication.Petra.Ac.IdG Setiawan, HN Palit, E SetyatiJurnal Infra, 2019•publication.Petra.Ac.Id. http://publication.petra.ac.id/index.php/teknik-informatika/article/view/8067
Setiawan, K. Y., Hidayat, H., & Gozali, A. (2014). Analisis User Opinion Twitter Pada Level Fine-grained Sentiment Analysis Terhadap Tokoh Publik. ..Telkomuniversity.Ac.IdK Setiawan, H Hidayati, A GozalieProceedings of Engineering, 2014•… .Telkomuniversity.Ac.Id. https://openlibrarypublications.telkomuniversity.ac.id/index.php/engineering/article/view/1487
Shinde, S., & Takalikar, M. (2016). A Literature Survey on Multilingual Sentiment Analysis. Academia.EduSA Shinde, MS Takalikaracademia.Edu. https://www.academia.edu/download/50298740/IJSTEV3I1016.pdf
Syarli, S., & Muin, A. (2016). Metode Naive Bayes Untuk Prediksi Kelulusan (Studi Kasus: Data Mahasiswa Baru Perguruan Tinggi). Fikom-Unasman.Ac.IdAA MuinJurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al, 2016•fikom-Unasman.Ac.Id, 2(1). https://fikom-unasman.ac.id/ejournal/index.php/jikom/article/view/76
Wenhua, Z., Hasan, M. K., Jailani, N. B., Islam, S., Safie, N., Albarakati, H. M., Aljohani, A., & Khan, M. A. (2024). A lightweight security model for ensuring patient privacy and confidentiality in telehealth applications. Computers in Human Behavior, 153, 108134. https://doi.org/10.1016/j.chb.2024.108134
Widianto, S. (2017). Rancang Bangun Aplikasi Telemedika untuk Pasien Diabetes Berbasis Platform iOS. Scholar.Archive.OrgSR WidiantoJurnal Elektra, 2017•scholar.Archive.Org, 2(1), 20. https://scholar.archive.org/work/csy6proa6vgmvn7wn2wncnciym/access/wayback/http://jurnal.pnj.ac.id/index.php/multinetics/article/download/1083/pdf
Yuninda, S. P., Aga Pasma, S., & Mantoro, T. (2022). Patient Data Security in Telemedicine Services from Data Misuse in Health Practice. 2022 IEEE 8th International Conference on Computing, Engineering and Design (ICCED), 1–4. https://doi.org/10.1109/ICCED56140.2022.10010685
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Andreas Andreas, Wella
This work is licensed under a Creative Commons Attribution 4.0 International License.
