Keywords:-

Keywords: Civil Servants; E-Stamps; Sentiment Analysis; Twitter/X; Support Vector Machine

Article Content:-

Abstract

Civil Servants play a crucial role in government administration and national development. The selection process for Civil Servant Candidates is a critical stage in civil servant management, as it has long-term implications for organizational effectiveness. With the advancement of digital technology, the Indonesian government has implemented electronic stamps in the civil servant candidate registration process to enhance efficiency and transparency. However, this policy has received various responses from the public, particularly on the social media platform Twitter. Many users express positive opinions regarding the convenience of administrative digitalization, while others report technical issues in using electronic stamps. Therefore, sentiment analysis is needed to understand public responses to this policy. One of the effective methods for sentiment classification is Support Vector Machine (SVM), which can optimally separate positive and negative opinions. This study utilizes a dataset comprising 1,249 reviews collected from Twitter/X. The best SVM model is selected through hyperparameter tuning using the GridSearchCV technique. The findings indicate that the SVM model with cost = 100 and gamma = 0.01 achieves the best performance, with an accuracy of 92%, precision of 86.48%, recall of 86.48%, F1-score of 86.48%, and a Kappa-Statistic of 81%.

References:-

References

Aisha, E., & Bahalwan, H. 2024. Twitter: Representasi Dan Ekspresi Diri Tanpa Sekat. Jurnal Visi Komunikasi, Vol. 22, No. 02, pp. 226-233.

Fahlevvi, M. R. 2022. Analisis Sentimen Terhadap Ulasan Aplikasi Pejabat Pengelola Informasi Dan Dokumentasi Kementerian Dalam Negeri Republik Indonesia Di Google Playstore Menggunakan Metode Support Vector Machine. Jurnal Teknologi Dan Komunikasi Pemerintahan, Vol. 4, No.1, pp. 1–13.

Herwinsyah, A. Witanti. 2022. Analisis Sentimen Masyarakat Terhadap Vaksinasi Covid-19 Pada Media Sosial Twitter Menggunakan Algoritma Support Vector Machine (Svm). Jurnal Sistem Informasi dan Informatika (Simika). Vol. 5, pp. 1.

Ratino, N. Hafidz, S. Anggreani, G. Wata. 2020. Sentimen Analisis Informasi Covid-19 menggunakan Support Vector Machine dan Naïve Bayes. Jurnal Jupiter. Vol. 12, No. 2, pp. 1-11.

Yusupa, A., & Tarigan, V. 2021. Perbandingan Algoritma Maching Learning dalam Analisis Sentimen Mobil Listrik di Indonesia pada Media Sosial Twitter/X. Jurnal Informatika Polinema. Vol. 10, No. 4, pp. 479-490.

Runimeirati, Muis, A., & Muhammad, F. 2023. Pelatihan Text Mining Menggunakan Bahasa Pemrograman Python. Abdimas Langkanae, Vol. 3, No. 1, pp. 36–46.

Feldman, R., & Sanger, J. 2007. The Text Mining Handbook: Advanced Approacehs in Analyzing Unstructured Data. New York: Cambridge University Press.

Syafa’aturrohman, A., Nurdiawan, O., Basysyar, F. M., & Sulaeman, M. 2024. Naive Bayes Meningkatkan Model Analisis Sentimen Pada Ulasan Aplikasi DANA Di Playstore Indonesia. Vol. 9, No. 2, pp. 171–180.

Rohim, A., Haviz Irfani, M., Ramadhan, M., & Ubaidillah, U. 2023. Penerapan Metode Text Mining dengan Chatbot Questions And Answer pada PT PLN (Persero) Sumatera Selatan. Klik - Jurnal Ilmu Komputer, Vol. 4, No. 2, pp. 59–67.

Nurjanah, W. E., Setya Perdana, R., & Fauzi, M. A. 2017. Analisis sentimen terhadap tayangan televisi berdasarkan opini masyarakat pada media sosial twitter menggunakan metode k-kearest neighbor dan pembobotan jumlah retweet. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, Vol. 1, No. 12, pp. 1750–1757.

Wang, L. 2005. Support Vector Machines: Theory and Applications. In Studies in Fuzziness and Soft Computing.

Nugroho, A. S., Witarto, A. B., & Handoko, D. 2003. Support Vector Machine. Proceedings of the 2011 Chinese Control and Decision Conference, CCDC 2011, pp. 842–847.

Cortes, C., & Vapnik, V. 1995. Support-Vector Networks CORINNA. Journal of Physics: Conference Series, pp. 273–297.

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., & Thirion, B. 2011 . Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, Vol. 12, pp. 2825–2830

Fide, S., Suparti, S., & Sudarno, S. 2021. Analisis Sentimen Ulasan Aplikasi Tiktok Di Google Play Menggunakan Metode Support Vector Machine (Svm) Dan Asosiasi. Jurnal Gaussian, Vol.10, No.3, pp. 346–358.

Aliyya, S. 2020. Analisis Sentimen Berbasis Aspek pada Ulasan Aplikasi Tokopedia Menggunakan Support Vector Machine. Skripsi. Program Studi Matematika Fakultas Sains dan Teknologi Universitas Islam Negeri Syaruf Hidayatullah.

Downloads

Citation Tools

How to Cite
Kamal, N. A., ., S., & Kartikasari, P. (2025). Support Vector Machine Implementation for Classifying Public Sentiment on Electronic Stamps use in Civil Servant Registration. International Journal Of Mathematics And Computer Research, 13(5), 5253-5259. https://doi.org/10.47191/ijmcr/v13i5.17