Sentiment Analysis on Satusehat Application Using Support Vector Machine Method
Abstract
Sentiment analysis is important in language processing and machine learning. SVM is proven to classify positive and negative sentiments with high accuracy effectively. SatuSehat application provides users with various health services and medical information, previously known as the PeduliLindungi Application. Once, this application was used to handle vaccination history used in the new normal era. Along the way, many problems arose due to the immaturity of the application after it was launched, which resulted in many user reviews being given through the Google Play Store application. Therefore, this study aims to determine SVM's performance in classifying user reviews of the SatuSehat application into positive and negative sentiments and to show visualization to find out the most frequent words from user reviews. Based on the research results, 25,000 data were divided into 18,359 negative class data and 6,641 positive class data. At the SVM classification stage, it produces a negative sentiment of 73.4% and a positive sentiment of 26.6%. In addition, the results of the SVM accuracy test obtained a result of 91% with a positive sentiment, namely having a precision test of 92%, a recall of 71%, and an f1-score of 80%, while for negative sentiment, namely having a precision test of 90%, a recall of 98% and f1-score of 94%. The visualization results found that the topics often appearing in positive reviews are good and sometimes great. In contrast, the negative reviews are update, difficult, strange, login, and bug.
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References
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[2] S. Hoskins et al., “Relative contribution of essential and non-essential activities to SARS-CoV-2 transmission following the lifting of public health restrictions in England and Wales,” Epidemiol. Infect., vol. 151, no. 3, pp. 1–8, 2023, doi: 10.1017/S0950268822001832.
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[13] M. I. Nurmansyah, C. Rosidati, Yustiyani, and N. M. Nasir, “Measuring the Success of PeduliLindungi Application Use for Supporting COVID-19 Prevention: A Case Study among College Students in Jakarta, Indonesia,” Kesmas, vol. 17, no. 1, pp. 11–16, 2022, doi: 10.21109/KESMAS.V17ISP1.6057.
[14] M. Shahroz et al., “COVID-19 digital contact tracing applications and techniques: A review post initial deployments,” Transp. Eng. J., vol. 5, no. 1, pp. 1–10, 2021, [Online]. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8132499/pdf/main.pdf
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[19] A. H. Alamoodi et al., “Sentiment analysis and its applications in fighting COVID-19 and infectious diseases: A systematic review,” Expert Syst. Appl., vol. 167, no. 1, pp. 1–13, 2020, doi: https://doi.org/10.1016/j.eswa.2020.114155.
[20] A. Mustopa, Hermanto, Anna, E. B. Pratama, A. Hendini, and D. Risdiansyah, “Analysis of User Reviews for the PeduliLindungi Application on Google Play Using the Support Vector Machine and Naive Bayes Algorithm Based on Particle Swarm Optimization,” in International Conference on Informatics and Computing (ICIC), Gorontalo, 2020, pp. 1–7. doi: 10.1109/ICIC50835.2020.9288655.
[21] A. Fathin, “Analisis sentimen terhadap ulasan aplikasi mobile menggunakan metode support vector machine (SVM) dan pendekatan lexicon based,” Universitas Islam Negeri Syarif Hidayatullah, 2022. [Online]. Available: https://repository.uinjkt.ac.id/dspace/bitstream/123456789/65009/1/AHMAD FATIHIN-FST.pdf
[22] K. Ahmad et al., “Sentiment Analysis of Users’ Reviews on COVID-19 Contact Tracing Apps with a Benchmark Dataset,” Comput. Lang., pp. 1–11, 2021, [Online]. Available: http://arxiv.org/abs/2103.01196
[23] Y. Al Amrani, M. Lazaar, and K. E. El Kadirp, “Random forest and support vector machine based hybrid approach to sentiment analysis,” Procedia Comput. Sci., vol. 127, no. 1, pp. 511–520, 2018, doi: 10.1016/j.procs.2018.01.150.
[24] Y. Mahendradhata, T. Lestari, and R. Djalante, “Strengthening government’s response to COVID-19 in Indonesia: A modified Delphi study of medical and health academics,” PLoS One, vol. 17, no. 9, pp. 1–20, 2022, doi: 10.1371/journal.pone.0275153.
[25] A. Mustopa, Hermanto, Anna, E. B. Pratama, A. Hendini, and D. Risdiansyah, “Analysis of User Reviews for the PeduliLindungi Application on Google Play Using the Support Vector Machine and Naive Bayes Algorithm Based on Particle Swarm Optimization,” in 2020 Fifth International Conference on Informatics and Computing (ICIC), 2020, pp. 1–7. doi: 10.1109/ICIC50835.2020.9288655.
Published
2023-07-08
How to Cite
[1]
Shahmirul Hafizullah Imanuddin, Kusworo Adi, and Rahmat Gernowo, “Sentiment Analysis on Satusehat Application Using Support Vector Machine Method ”, j.electron.electromedical.eng.med.inform, vol. 5, no. 3, pp. 143-149, Jul. 2023.
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Electronics
Copyright (c) 2023 Shahmirul Hafizullah Imanuddin, Kusworo Adi , Rahmat Gernowo
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