Analisis Sentimen Komentar TikTok terhadap Kebijakan Larangan Wisuda Sekolah oleh Gubernur Jawa Barat Menggunakan Algoritma Naive Bayes
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Sep 10, 2025
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This research focuses on analyzing public sentiment regarding the policy banning school graduation ceremonies in West Java, which aims to adapt the tradition to socio-economic conditions. This research is expected to provide insights for the formulation of more inclusive and equitable education policies, as well as offer alternative, more friendly graduation practices. The research object includes public opinions and comments on the social media app TikTok regarding this policy. The method used is sentiment analysis with the Naive Bayes algorithm, with data collected over two months following the policy's announcement. The results show that the majority of respondents support the ban, driven by reasons of reducing the burden on parents' costs and minimizing social jealousy. The conclusion can be drawn that this policy has received a positive response from the public. These findings can serve as a reference for policymakers in developing a more equitable education system. It is recommended that the government continue to involve the public in the policy-making process and seek alternative methods for celebrating graduations that are more economical and equitable.
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Setiawan, K., & Alafia, E. (2025). Analisis Sentimen Komentar TikTok terhadap Kebijakan Larangan Wisuda Sekolah oleh Gubernur Jawa Barat Menggunakan Algoritma Naive Bayes. Jurnal Indonesia : Manajemen Informatika Dan Komunikasi, 6(3), 1834-1841. https://doi.org/10.63447/jimik.v6i3.1615
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References
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Surahman, A., Octaniansyah, A. F., & Darwis, D. (2020). Teknologi Web Crawler Sebagai Alat Pengembangan Market Segmentasi Untuk Mencapai Keunggulan Bersaing Pada E-Marketplace. Jurnal Komputer Dan Informatika, 15(1), 20-28.
Tanggraeni, A. I., & Sitokdana, M. N. (2022). Analisis Sentimen Aplikasi E-Government pada Google Play Menggunakan Algoritma Naïve Bayes. JATISI (Jurnal Teknik Informatika dan Sistem Informasi), 9(2), 785-795. https://doi.org/10.35957/jatisi.v9i2.1835.
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Zakira, A., Arhami, M., Abdi, M. I., & Safriadi, S. (2025). Text-Based Emotion Sentiment Analysis on Social Media Using NLP and Lexicon Approach (Case Study: Gaza Conflict). Journal of Informatics Engineering and Software Applications, 1(1), 139-149.
Asghar, M. Z., Ahmad, S., Marwat, A., & Kundi, F. M. (2015). Sentiment analysis on youtube: A brief survey. arXiv preprint arXiv:1511.09142. https://doi.org/10.48550/arXiv.1511.09142.
Chen, H., Hu, S., Hua, R., & Zhao, X. (2021). Improved naive Bayes classification algorithm for traffic risk management. EURASIP Journal on Advances in Signal Processing, 2021(1), 30.
Darwis, D., Siskawati, N., & Abidin, Z. (2021). Penerapan Algoritma Naive Bayes Untuk Analisis Sentimen Review Data Twitter Bmkg Nasional. Jurnal Tekno Kompak, 15(1), 131-145.
Devita, R. N., Herwanto, H. W., & Wibawa, A. P. (2018). Perbandingan kinerja metode naive bayes dan k-nearest neighbor untuk klasifikasi artikel berbahasa indonesia. J. Teknol. Inf. dan Ilmu Komput, 5(4).
Fricco, A., & Setiawan, K. (2023). Implementasi Algoritma Naive Bayes Menggunakan Particle Swarm Optimization Untuk Prediksi Penjualan Produk Air Mineral. Jurnal Sains dan Teknologi, 5(1), 193-197.
Kevin, K., Enjeli, M., & Wijaya, A. (2024). Analisis Sentimen Pengunaaan Aplikasi Kinemaster Menggunakan Metode Naive Bayes. Jurnal Ilmiah Computer Science, 2(2), 89-98. https://doi.org/10.58602/jics.v2i2.24.
Lestari, S., & Saepudin, S. (2021, September). Analisis sentimen vaksin sinovac pada twitter menggunakan algoritma Naive Bayes. In Prosiding Seminar Nasional Sistem Informasi dan Manajemen Informatika Universitas Nusa Putra (Vol. 1, pp. 163-170).
Normawati, D., & Prayogi, S. A. (2021). Implementasi Naïve Bayes classifier dan confusion matrix pada analisis sentimen berbasis teks pada Twitter. J-SAKTI (Jurnal Sains Komputer dan Informatika), 5(2), 697-711. https://dx.doi.org/10.30645/j-sakti.v5i2.369.
Nurtikasari, Y., Alam, S., & Hermanto, T. I. (2022). Analisis Sentimen Opini Masyarakat Terhadap Film Pada Platform Twitter Menggunakan Algoritma Naive Bayes. INSOLOGI: Jurnal Sains dan Teknologi, 1(4), 411-423. https://doi.org/10.55123/insologi.v1i4.770.
Permatasari, R. I., Fauzi, M. A., Adikara, P. P., & Sari, E. D. L. (2018, November). Twitter sentiment analysis of movie reviews using ensemble features based Naïve Bayes. In 2018 International Conference on Sustainable Information Engineering and Technology (SIET) (pp. 92-95). IEEE. https://doi.org/10.1109/SIET.2018.8693195.
Prasetyo, S. D., Hilabi, S. S., & Nurapriani, F. (2023). Analisis Sentimen Relokasi Ibukota Nusantara Menggunakan Algoritma Naïve Bayes dan KNN. Jurnal KomtekInfo, 1-7. https://doi.org/10.35134/komtekinfo.v10i1.330.
Putri, D. D., Nama, G. F., & Sulistiono, W. E. (2022). Analisis Sentimen Kinerja Dewan Perwakilan Rakyat (DPR) Pada Twitter Menggunakan Metode Naive Bayes Classifier. Jurnal Informatika dan Teknik Elektro Terapan, 10(1). https://doi.org/10.23960/jitet.v10i1.2262.
Ratnawati, F. (2018). Implementasi Algoritma Naive Bayes Terhadap Analisis Sentimen Opini Film Pada Twitter. Jurnal Inovtek Polbeng Seri Informatika, 3(1), 50-59.
Saputra, C., Wilcent, W., Irsyad, H., & Rahman, A. (2025). Pemrosesan Query dan Pemeringkatan Judul Berita Terkait Gubernur Jawa Barat Menggunakan TF-IDF dan Cosine Similarity. Applied Information Technology and Computer Science (AICOMS), 4(1), 25-32.
Surahman, A., Octaniansyah, A. F., & Darwis, D. (2020). Teknologi Web Crawler Sebagai Alat Pengembangan Market Segmentasi Untuk Mencapai Keunggulan Bersaing Pada E-Marketplace. Jurnal Komputer Dan Informatika, 15(1), 20-28.
Tanggraeni, A. I., & Sitokdana, M. N. (2022). Analisis Sentimen Aplikasi E-Government pada Google Play Menggunakan Algoritma Naïve Bayes. JATISI (Jurnal Teknik Informatika dan Sistem Informasi), 9(2), 785-795. https://doi.org/10.35957/jatisi.v9i2.1835.
Wahyuni, W. (2022). Analisis Sentimen terhadap Opini Feminisme Menggunakan Metode Naive Bayes. Jurnal Informatika Ekonomi Bisnis, 148-153. https://doi.org/10.37034/infeb.v4i4.162.
Zakira, A., Arhami, M., Abdi, M. I., & Safriadi, S. (2025). Text-Based Emotion Sentiment Analysis on Social Media Using NLP and Lexicon Approach (Case Study: Gaza Conflict). Journal of Informatics Engineering and Software Applications, 1(1), 139-149.