Analisis Sentimen Masyarakat terhadap Instruksi Gubernur Jakarta tentang ASN Wajib Gunakan Transportasi Umum Menggunakan Algoritma Support Vector Machine
Article Sidebar
Article Statistics
36
Total Views13
Total DownloadsMain Article Content
Abstract
Article Summary
Social media has become the primary means for people to express their opinions on public policies. One policy that has generated mixed reactions is DKI Jakarta Governor Instruction Number 6 of 2025, which requires civil servants (ASN) to use public transportation every Wednesday. This study aims to analyze public sentiment towards this policy using the Support Vector Machine (SVM) algorithm. Data was collected from social media X using purposive sampling technique using related keywords during the period May-July 2025. Then processed through the stages of cleansing, tokenizing, stopword removal, stemming, and transformation into numerical form using TF-IDF. Of the 800 tweets collected, 600 positive opinions and 200 negative opinions were obtained. Then, the data was classified using the Support Vector Machine algorithm. Thus, the research that has been conducted obtained an accuracy of 76.00%, and an AUC of 0.725. These findings are expected to not only provide accurate sentiment classification, but also provide practical input for the government to evaluate the effectiveness of policies, improve socialization strategies, and measure the level of public acceptance of ASN transportation policies.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
Dani, A. H., Puspaningrum, E. Y., & Mumpuni, R. (2024). Studi Performa TF-IDF dan Word2Vec Pada Analisis Sentimen Cyberbullying. Router: Jurnal Teknik Informatika dan Terapan, 2(2), 94-106. https://doi.org/10.62951/router.v2i2.76.
Effendy, V., Novantirani, A., & Sabariah, M. K. (2016). Sentiment analysis on Twitter about the use of city public transportation using support vector machine method. Int. J. Inf. Commun. Technol, 2(1), 57.
Ernyasih, E., Mallongi, A., Fauziah, M., Munggaran, G. A., Salsabilla, F. A., Madhoun, W. A., ... & Taufiqurrochman, T. (2025). Urban Transportation and Rising CO Emissions: A Case Study of East Jakarta and its Public Health Impacts. Journal of Public Health and Pharmacy, 5(1), 150-157. https://doi.org/10.56338/jphp.v5i1.6823.
Gifari, O. I., Adha, M., Hendrawan, I. R., & Durrand, F. F. S. (2022). Analisis Sentimen Review Film Menggunakan TF-IDF dan Support Vector Machine. Journal of Information Technology, 2(1), 36-40. https://doi.org/10.46229/jifotech.v2i1.330.
Kurniawan, W., Arham, M., & Muluk, H. (2024). Social Media's Influence on Political Participation: Insights from a Systematic Review and Meta-Analysis in Indonesian Psychology. Jurnal Psikologi, 51(3). https://doi.org/10.22146/jpsi.101469.
Lim, T., & Ong, C. S. (2021). Portfolio diversification using shape-based clustering. Journal of Financial Data Science, 3(1), 111. https://doi.org/10.3905/jfds.2020.1.054.
Mulyana, D. I., & Lutfianti, N. (2023). Penerapan Sentimen Analisis Dengan Algoritma SVM Dalam Tanggapan Netizen Terhadap Berita Resesi 2023. Sisfotenika, 13(1), 53-64. https://doi.org/10.30700/jst.v13i1.1339.
Nanditho, G. A., & Yola, L. (2022). Urban development and traffic congestion: Jakarta study during the pandemic. In Sustainable Development Approaches: Selected Papers of AUA and ICSGS 2021 (pp. 135-142). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-99979-7_16.
North, M. (2012, August). Data mining for the masses.
Nugraha, T., Purwantoro, P., & Umaidah, Y. (2022). Analisis Sentimen terhadap Perpanjangan Masa Jabatan Presiden Indonesia Menggunakan Algoritma Naïve Bayes. Jurnal Pendidikan dan Konseling (JPDK), 4(4), 4625-4635.
Pradana, M., Silvianita, A., Syarifuddin, S., & Renaldi, R. (2022). The implication of digital organisational culture on firm performance. Frontiers in Psychology, 13, 840699. https://doi.org/10.3389/fpsyg.2022.840699.
Purwanti, Z. (2024). Pemodelan Text Mining untuk Analisis Sentimen Terhadap Program Makan Siang Gratis di Media Sosial X Menggunakan Algoritma Support Vector Machine (SVM). Jurnal Indonesia: Manajemen Informatika Dan Komunikasi, 5(3), 3065-3079.
Sahir, S. H. (2021). Metodologi penelitian. Penerbit KBM Indonesia.
Sarimole, F. M., & Kudrat, K. (2024). Analisis Sentimen Terhadap Aplikasi Satu Sehat Pada Twitter Menggunakan Algoritma Naive Bayes Dan Support Vector Machine. Jurnal Sains dan Teknologi, 5(3), 783-790. https://doi.org/10.55338/saintek.v5i1.2702.
Styawati, S., Hendrastuty, N., & Isnain, A. R. (2021). Analisis sentimen masyarakat terhadap program kartu prakerja pada twitter dengan metode support vector machine. Jurnal Informatika: Jurnal Pengembangan IT, 6(3), 150-155. https://doi.org/10.30591/jpit.v6i3.2870.
Thet, T. T., Na, J. C., & Khoo, C. S. (2010). Aspect-based sentiment analysis of movie reviews on discussion boards. Journal of information science, 36(6), 823-848. https://doi.org/10.1177/0165551510388123.