Sentiment Analysis of Public Service Using Naïve Bayes Classifier

Arga Aditia Purnama; Yoannes Romando Sipayung

doi:10.51519/journalisi.v7i3.1207

Arga Aditia Purnama Universitas Ngudiwaluyo, Indonesia
Yoannes Romando Sipayung Universitas Ngudiwaluyo, Indonesia

DOI: 10.51519/journalisi.v7i3.1207

Keywords: Sentiment Analysis, Text Mining, Naïve Bayes, Public Service Quality

Abstract

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

Download data is not yet available.

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.