Sentiment Analysis of Public Service Using Naïve Bayes Classifier
DOI:
https://doi.org/10.51519/journalisi.v7i3.1207Keywords:
Sentiment Analysis, Text Mining, Naïve Bayes, Public Service QualityAbstract
Public administrative service quality is a crucial factor in citizen satisfaction. This study analyzes sentiment in public service reviews using a text mining approach with the Naïve Bayes Classifier method. The dataset was collected from citizen feedback on online platforms regarding public administrative services. Preprocessing steps included tokenization, case folding, stopword removal, and stemming. The Naïve Bayes algorithm with Laplace smoothing was applied for classification, and performance was evaluated using accuracy, precision, recall, and F1-score. The experiment resulted in an accuracy of 91.2%, precision of 90.3%, recall of 89.7%, and F1-score of 90.0%. The analysis revealed that Service Speed obtained an average score of 3.21, indicating a moderate level of citizen satisfaction in that aspect. These findings suggest that while the Naïve Bayes method is effective for sentiment classification, its greatest value lies in providing actionable insights for public service improvement. Specifically, policymakers can prioritize addressing delays in service speed through simplified procedures, improved staffing, and digital innovation, while maintaining strengths such as officer politeness and effective complaint handling. By leveraging sentiment analysis, public institutions can continuously monitor citizen feedback, identify problem areas, and implement evidence-based strategies to enhance service quality and strengthen public trust.
Downloads
References
A. Dwiyanto, Mewujudkan Good Governance melalui Pelayanan Publik. Yogyakarta: Gadjah Mada University Press, 2008.
B. Gunawan, H. Sastypratiwi, and E. E. Pratama, "Sistem analisis sentimen pada ulasan produk menggunakan metode Naive Bayes," J. Edukasi Penelit. Informatika (JEPIN), vol. 4, no. 2, pp. 113–118, 2018.
E. T. Handayani and A. Sulistiyawati, "Analisis sentimen respon masyarakat terhadap kabar harian Covid-19 pada Twitter Kementerian Kesehatan dengan metode klasifikasi Naive Bayes," J. Teknol. Sistem Inform. (JTSI), vol. 2, no. 3, pp. 32–37, 2021.
M. S. Hudin, M. A. Fauzi, and S. Adinugroho, "Implementasi metode text mining dan K-Means clustering untuk pengelompokan dokumen skripsi (Studi kasus: Universitas Brawijaya)," J. Pengembangan Teknol. Inform. Ilmu Komput. (JPTIIK), vol. 2, no. 11, pp. 5518–5524, 2018.
R. N. Mauliza and Y. R. Sipayung, "Penerapan text mining dalam menganalisis pendapat masyarakat terhadap Pemilu 2024 pada media sosial X menggunakan metode Naive Bayes," Technomedia J., vol. 9, no. 1, pp. 1–16, 2024.
C. D. Manning, P. Raghavan, and H. Schütze, Introduction to Information Retrieval. New York: Cambridge University Press, 2008.
A. Nurian, T. N. Padilah, and G. Garno, "Analisis sentimen terhadap pelayanan Disdukcapil Karawang menggunakan Naive Bayes Classifier," J. Informatika Teknik Elektro Terapan (JITE), vol. 12, no. 2, 2024.
OECD, Serving Citizens: Strategies for Better Service Delivery (OECD Public Governance Reviews). Paris: OECD Publishing, 2017.
H. Pasolong, Teori Administrasi Publik. Bandung: Alfabeta, 2019.
B. Pang and L. Lee, "Opinion mining and sentiment analysis," Found. Trends Inf. Retr., vol. 2, no. 1–2, pp. 1–135, 2008.
M. P. R. Putra and K. R. N. Wardani, "Penerapan text mining dalam menganalisis kepribadian pengguna media sosial," JUTIM (Jurnal Teknik Informatika Musirawas), vol. 5, no. 1, pp. 63–71, 2020.
M. Rahman, Ilmu Administrasi, vol. 1. Jakarta: Sah Media, 2017.
L. Zhang, S. Wang, and B. Liu, "Deep learning for sentiment analysis: A survey," Wiley Interdiscip. Rev. Data Mining Knowl. Discov., vol. 8, no. 4, e1253, 2018.
R. Tasi, "Improving the quality of public services through bureaucratic reformation: Human right perspectives," Jurnal Ham, vol. 13, p. 589, 2022.
E. Hasibuan and E. A. Heriyanto, "Analisis Sentimen Pada Ulasan Aplikasi Amazon Shopping Di Google Play Store Menggunakan Naive Bayes Classifier," J. Teknik dan Science, vol. 1, no. 3, pp. [page numbers], 2023.
M. Syarifuddinn, "Analisis Sentimen Opini Publik Mengenai Covid-19 Pada Twitter Menggunakan Metode Naïve Bayes Dan Knn," Inti Nusa Mandiri, vol. 15, no. 1, pp. 23–28, 2020.
N. P. G. Naraswati, R. Nooraeni, D. C. Rosmilda, D. Desinta, F. Khairi, and R. Damaiyanti, "Analisis Sentimen Publik dari Twitter Tentang Kebijakan Penanganan Covid-19 di Indonesia dengan Naive Bayes Classification," Sistemasi, vol. 10, no. 1, pp. 222–238, 2021.
A. L. Fairuz, R. D. Ramadhani, and N. A. F. Tanjung, "Analisis Sentimen Masyarakat Terhadap COVID-19 Pada Media Sosial Twitter," Journal of Dinda: Data Sci., Inf. Technol. Data Anal., vol. 1, no. 1, pp. 42–51, 2021.
A. Yadollahi, A. G. Shahraki, and O. R. Zaiane, "Current state of text sentiment analysis from opinion to emotion mining," ACM Comput. Surv., vol. 50, no. 2, pp. 1–33, 2017.
A. Henuk-Kacaribu, Pengantar Ilmu Administrasi. Yogyakarta, Indonesia: Penerbit Andi, 2020.
K. R. N. Wardani, "Penerapan Text Mining dalam Menganalisis Kepribadian Pengguna Media Sosial," 2022.
I. Rodiyah, H. Sukmana, and L. Mursyidah, Buku Ajar Pengantar Ilmu Administrasi Publik. Sidoarjo, Indonesia: Umsida Press, 2021, pp. 1–92.
A. Yadav and D. K. Vishwakarma, "Sentiment analysis using deep learning architectures: A review," Artif. Intell. Rev., vol. 53, no. 6, pp. 4335–4385, 2020.
G. Rakasiwi, "Bureaucratic reform in the field of human resources apparatus as the main capital in realizing good governance," in Proc. 5th Int. Conf. Indonesian Social Political Enquiries (ICISPE 2020), Semarang, Indonesia, Oct. 9–10, 2020, published Jan. 2021.
M. Brezzi, S. González, D. Nguyen, and M. Prats, "An updated OECD framework on drivers of trust in public institutions to meet current and future challenges," OECD Working Papers Public Governance, no. 48, pp. 1–60, 2021.
Downloads
Published
Issue
Section
License
Authors Declaration
- The Authors certify that they have read, understood, and agreed to the Journal of Information Systems and Informatics (JournalISI) submission guidelines, policies, and submission declaration. The submission has been prepared using the provided template.
- The Authors certify that all authors have approved the publication of this manuscript and that there is no conflict of interest.
- The Authors confirm that the manuscript is their original work, has not received prior publication, is not under consideration for publication elsewhere, and has not been previously published.
- The Authors confirm that all authors listed on the title page have contributed significantly to the work, have read the manuscript, attest to the validity and legitimacy of the data and its interpretation, and agree to its submission.
- The Authors confirm that the manuscript is not copied from or plagiarized from any other published work.
- The Authors declare that the manuscript will not be submitted for publication in any other journal or magazine until a decision is made by the journal editors.
- If the manuscript is finally accepted for publication, the Authors confirm that they will either proceed with publication immediately or withdraw the manuscript in accordance with the journal’s withdrawal policies.
- The Authors agree that, upon publication of the manuscript in this journal, they transfer copyright or assign exclusive rights to the publisher, including commercial rights
