Sentiment Analysis of e-Government Service Using the Naive Bayes Algorithm
DOI:
https://doi.org/10.30812/matrik.v23i2.3272Keywords:
Classfier, Google Playstore, Naïve Bayes, Opinion Mining, Sentiment AnalysisAbstract
E-Government which involves the use of communication and information technology to provide Public services have three obstacles. One of these obstacles is the implementation of e-Government by autonomous regional governments is still carried out individually. Apart from that, implementing the website regions are also not supported by efficient management systems and work processes, this is partly the case This is largely due to the lack of preparation of regulations, procedures and limited resources man. Apart from that, many local governments consider implementing e-Government only involves developing local government websites. More precisely, the implementation of e-Government It is only limited to the maturity stage and ignores the three other important stages that need to be completed. The aim of this research is to determine the level of public approval for government application services. This research uses the Naive Bayes Classifier approach as the methodology. The data sources used in this research consist of user reviews and comments obtained from Google Play Store. The results of this investigation produce a level of precision The highest is achieving a score of 83%. Additionally it shows an accuracy rate of 83%,level
completeness is 100%, and F-measure is 90.7%.
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References
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[20] M. K. Khoirul Insan, U. Hayati, and O. Nurdiawan, “Analisis Sentimen Aplikasi Brimo Pada Ulasan Pengguna Di Google Play Menggunakan Algoritma Naive Bayes,†JATI (Jurnal Mhs. Tek. Inform., vol. 7, no. 1, pp. 478–483, 2023, doi: 10.36040/jati.v7i1.6373.
[2] A. I. Tanggraeni and M. N. N. Sitokdana, “Analisis Sentimen Aplikasi E-Government pada Google Play Menggunakan Algoritma Naïve Bayes,†JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 9, no. 2, pp. 785–795, 2022, doi: 10.35957/jatisi.v9i2.1835.
[3] G. Y. Iskandarli, “Applying Clustering and Topic Modeling to Automatic Analysis of Citizens’ Comments in EGovernment,†Int. J. Inf. Technol. Comput. Sci., vol. 12, no. 6, pp. 1–10, 2020, doi: 10.5815/ijitcs.2020.06.01.
[4] A. Akram, N. Risal, D. Rezky, and A. Sulaiman, “Classification of Sentiment Analysis and Community Opinion Modeling Topics for Application of ICT in Government Operations,†vol. 5, no. 1, pp. 35–44, 2023, doi: https://doi.org/10.55151/ijeedu.v5i1.99.
[5] I. S. Jami and A. S. Zubair, “Semantic Web based E-Government System,†Indian J. Sci. Technol., vol. 11, no. 44, pp. 1–6, 2018, doi: 10.17485/ijst/2018/v11i44/132332.
[6] D. Joshi, M. Khalegaonkar, M. Lohikpure, P. Maan, and R. A. Deshmukh, “Sentimental Analysis on E-Governance,†Int. J. Innov. Res. Sci. Eng., vol. Vol.3, no. Issue 05, pp. 1–9, 2017, doi: http://ijirse.com/wp-content/upload/2017/03/PY2087ijirse.pdf.
[7] M. R. Fahlevvi, “Analisis Sentimen Terhadap Ulasan Aplikasi Pejabat Pengelola Informasi Dan Dokumentasi Kementerian Dalam Negeri Republik Indonesia Di Google Playstore Menggunakan Metode Support Vector Machine,†J. Teknol. dan Komun. Pemerintah., vol. 4, no. 1, pp. 1–13, 2022, doi: 10.33701/jtkp.v4i1.2701.
[8] A. Filemon, H. Kaban, and N. Yudistira, “Analisis Sentimen Aplikasi E-Goverment berdasarkan Ulasan Pengguna menggunakan Metode Maximum Entropy dan Seleksi Fitur Mutual Information,†J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 5, no. 4, pp. 1452–1458, 2021, doi: https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/8877.
[9] M. K. Anam, B. N. Pikir, and M. B. Firdaus, “Penerapan Na ̈ıve Bayes Classifier, K-Nearest Neighbor (KNN) dan Decision Tree untuk Menganalisis Sentimen pada Interaksi Netizen danPemeritah,†MATRIK J. Manajemen, Tek. Inform. dan Rekayasa Komput., vol. 21, no. 1, pp. 139–150, 2021, doi: 10.30812/matrik.v21i1.1092.
[10] S. Abdelgaber, S. Abdel Gaber, and B. Kazim, “A Proposed Road Map To Enhance E-Government Services: Kuwait Case Study,†Researchgate.Net, no. December 2019, 2019, doi: https://www.researchgate.net/publication/339237461_A_Proposed_Road_Map_To_Enhance_E-Government_Services_Kuwait_Case_Study.
[11] R. I. Syah, Hoiriyah, and M. Walid, “Analisis Sentimen Pengguna Media Sosial Terhadap Aplikasi M-Health Peduli Lindungi dengan Metode Lexicon Based dan Naive Bayes,†vol. 3, no. 2, pp. 54–60, 2020, doi: http://dx.doi.org/10.21927/ijubi.v6i1.3275.
[12] T. Tinaliah and T. Elizabeth, “Analisis Sentimen Ulasan Aplikasi PrimaKu Menggunakan Metode Support Vector Machine,†JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 9, no. 4, pp. 3436–3442, 2022, doi: 10.35957/jatisi.v9i4.3586.
[13] R. Apriani et al., “Analisis Sentimen dengan Naïve Bayes Terhadap Komentar Aplikasi Tokopedia,†J. Rekayasa Teknol. Nusa Putra, vol. 6, no. 1, pp. 54–62, 2019.
[14] A. Faesal, A. Muslim, A. H. Ruger, and K. Kusrini, “Sentimen Analisis Terhadap Komentar Konsumen Terhadap Produk Penjualan Toko Online Menggunakan Metode K-Means,†MATRIK J. Manajemen, Tek. Inform. dan Rekayasa Komput., vol. 19, no. 2, pp. 207–213, 2020, doi: 10.30812/matrik.v19i2.640.
[15] I. S. K. Idris, Y. A. Mustofa, and I. A. Salihi, “Analisis Sentimen Terhadap Penggunaan Aplikasi Shopee Mengunakan Algoritma Support Vector Machine (SVM),†Jambura J. Electr. Electron. Eng., vol. 5, no. 1, pp. 32–35, 2023, doi: 10.37905/jjeee.v5i1.16830.
[16] I. Di Estika, I. Darmawan, and O. N. Pratiwi, “Analisis Sentimen Ulasan Aplikasi Buka Lapak Untuk Peningkatan Layanan Menggunakan Algoritma Naive Bayes,†vol. 8, no. 5, pp. 4367–4376, 2021, doi: https://openlibrarypublications.telkomuniversity.ac.id/index.php/engineering/article/view/14676.
[17] S. Surohman, S. Aji, R. Rousyati, and F. F. Wati, “Analisa Sentimen Terhadap Review Fintech Dengan Metode Naive Bayes Classifier Dan K- Nearest Neighbor,†EVOLUSI J. Sains dan Manaj., vol. 8, no. 1, pp. 93–105, 2020, doi: 10.31294/evolusi.v8i1.7535.
[18] A. Athallah Muhammad et al., “Analisis Sentimen Pengguna Aplikasi Dana Berdasarkan Ulasan Ada Google Play Menggunakan Metode Support Vector Machine,†pp. 194–204, 2022, doi: https://conference.upnvj.ac.id/index.php/senamika/article/view/2171/1660.
[19] I. Atoum, “A novel framework for measuring software quality-in-use based on semantic similarity and sentiment analysis of software reviews,†J. King Saud Univ. - Comput. Inf. Sci., vol. 32, no. 1, pp. 113–125, 2020, doi: 10.1016/j.jksuci.2018.04.012.
[20] M. K. Khoirul Insan, U. Hayati, and O. Nurdiawan, “Analisis Sentimen Aplikasi Brimo Pada Ulasan Pengguna Di Google Play Menggunakan Algoritma Naive Bayes,†JATI (Jurnal Mhs. Tek. Inform., vol. 7, no. 1, pp. 478–483, 2023, doi: 10.36040/jati.v7i1.6373.
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Published
2024-03-26
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Section
Articles
How to Cite
[1]
W. purbaratri, H. D. Purnomo, D. Manongga, I. Setyawan, and H. Hendry, “Sentiment Analysis of e-Government Service Using the Naive Bayes Algorithm”, MATRIK, vol. 23, no. 2, pp. 441–452, Mar. 2024, doi: 10.30812/matrik.v23i2.3272.
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