ANALISIS OPINI PUBLIK DI TIKTOK TERHADAP KEBIJAKAN PEMBATASAN USIA PENGGUNA MEDIA SOSIAL: STUDI PADA KOLOM KOMENTAR
DOI:
https://doi.org/10.23969/jp.v11i03.62283Keywords:
beginning reading ability, whole language model reading aloud type, elementary educationAbstract
This study aims to analyze public opinion on TikTok regarding the age restriction policy. This study uses a descriptive qualitative approach, with data sources consisting of user comments on TikTok video content discussing the policy. Data were collected through documentation techniques, taking 37 relevant comments. Data analysis was conducted using content analysis, classifying comments into positive, negative, and neutral sentiment categories. The results show that negative sentiment dominated at 48.6%, followed by positive sentiment at 40.5%, and neutral sentiment at 10.8%. These findings indicate that the age restriction policy for social media users continues to generate debate among the public, particularly regarding issues of individual freedom and child protection.
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Ainur, G., Latifah, N., & Nugraha, F. (2024). Analisis sentimen publik terhadap program makan bergizi gratis menggunakan metode Random Forest pada platform X. Jurnal SITECH. https://doi.org/10.24176/sitech.v8i1.15628
Paridy Man, G. M. C., Sinlae, A. A. J., & Ngaga, E. (2024). Analisis sentimen di media sosial X tentang IKN dengan Naïve Bayes. Jurnal Informatika Polinema. https://doi.org/10.33795/jip.v11i4.7246
Dayani, A. D., Yuhandri, Y., & Nurcahyo, G. W. (2023). Analisis sentimen terhadap opini publik pada sosial media Twitter menggunakan metode SVM. Jurnal KomtekInfo. https://doi.org/10.35134/komtekinfo.v11i1.439
Bowen, G. A. (2009). Document analysis as a qualitative research method. Qualitative Research Journal, 9(2), 27–40. https://doi.org/10.3316/QRJ0902027
Cheng, Z., & Li, Y. (2023). Like, comment, and share on TikTok: Exploring the effect of sentiment and second-person view on user engagement with TikTok news videos. Social Science Computer Review. https://doi.org/10.1177/08944393231178603
Ariyus, D., Manongga, D., & Sembiring, I. (2024). Enhancing sentiment analysis of Indonesian tourism video content commentary on TikTok: A FastText and Bi-LSTM approach. Engineering, Technology & Applied Science Research, 14(6), 18020–18028. https://doi.org/10.48084/etasr.8859
Aditia, G. A. S., & Putri, L. A. A. R. (2026). Analisis sentimen pengguna TikTok terhadap progres pembangunan IKN menggunakan LSTM dan FastText. Jurnal Nasional Teknologi Informasi dan Aplikasinya, 4(2). https://doi.org/10.24843/JNATIA.2026.v04.i02.p21
Christian, C., & Maghfiratunnisa, A. (2025). Analisis sentimen masyarakat terhadap konten bahasa isyarat pada media sosial TikTok menggunakan RNN dan BERT. Jurnal Repositor, 7(2). https://doi.org/10.22219/repositor.v7i2.33615
Faqia, Z., Sibaroni, Y., & Prasetiyowati, S. S. (2024). Sentiment analysis on TikTok app using Long Short-Term Memory (LSTM) with stochastic gradient descent optimization. Jurnal Media Informatika Budidarma, 8(3). https://doi.org/10.30865/mib.v8i3.7699
Zhang, C. (2025). Optimisation of social media marketing communication strategies based on user sentiment analysis: TikTok as an example. Applied and Environmental Mathematical Sciences. https://doi.org/10.54254/2754-1169/2025.BJ25113
Ardiansyah, D., Saepudin, A., Aryanti, R., Fitriani, E., & Royadi, R. (2023). Analisis sentimen review pada aplikasi media sosial TikTok menggunakan algoritma K-NN dan SVM berbasis PSO. Jurnal Informatika Kaputama, 7(2). https://doi.org/10.59697/jik.v7i2.148
Ismail, U. H., & Alamanda, D. T. (2025). Analyzing user sentiment toward the new TikTok Go feature in Indonesia. Indonesian Journal of Digital Business. https://doi.org/10.17509/ijdb.v5i1.83068
Ariyus, D., et al. (2024). Sentiment analysis as a method to measure public opinion on social media interactions. Engineering, Technology & Applied Science Research. https://doi.org/10.48084/etasr.8859
Procedia Computer Science. (2025). Sentiment analysis of TikTok user comments on QRIS adoption in Indonesia using IndoBERT. Procedia Computer Science, 269, 121–130. https://doi.org/10.1016/j.procs.2025.08.265
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