Analisis Strategi Komunikasi Krisis PR Pertamina dalam Pemulihan Citra Berbasis Machine Learning
DOI:
https://doi.org/10.71282/jurmie.v2i12.1350Keywords:
Crisis Communication, Public Relations, BBM, Adulterated Gasoline, Sentiment Analysis, Machine Learning, SCCTAbstract
This study analyzes Pertamina's public relations crisis communication strategy in responding to the issue of “fake fuel” that went viral on social media in 2025. The rapid spread of public complaints, allegations of fuel adulteration, and increasing criticism on platforms such as X and TikTok had a significant impact on the company's reputation, requiring a data-driven approach to crisis communication. Unlike previous studies, which generally relied on manual content reading, this study offers a new approach by integrating machine learning-based sentiment analysis to map public perceptions in a more structured manner. Data was collected by gathering 1,000 posts on the X platform and supplemented with monitoring data from Brand24. All texts were processed using Natural Language Processing (NLP) techniques and classified with a Support Vector Machine (SVM) algorithm verified through ten iterations of Monte Carlo Cross-Validation. This model produced an average accuracy of 0.559 and showed a strong dominance of negative sentiment in 603 posts. Analysis of public engagement on TikTok showed a variety of responses, ranging from support for fuel distribution activities to sharp criticism of service quality and operations at fuel filling stations.These findings indicate that Pertamina's crisis communication strategy has not been entirely successful in reducing negative public perception. Theoretically, this research contributes by integrating machine learning data into SCCT analysis, thereby providing a more accurate understanding of public responses. Practically, the results of this study are expected to help energy companies improve their crisis communication strategies to be more responsive and effective.
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Zein, A., Farizy, S., & Suharyanto, E. (2022). Sentimen Analisis Pada Komentar Pendek Evaluasi Dosen Oleh Mahasiswa (Edom) Program Studi Sistem Informasi Universitas Pamulung. Jurnal Ilmu Komputer, 5(01), 17–23. https://jurnal.pranataindonesia.ac.id/index.php/jik/article/view/113%0Ahttps://jurnal.pranataindonesia.ac.id/index.php/jik/article/download/113/66
Amandasari, F., & Damayanti. (2025). Perbandingan Kinerja Support Vector Machine dan Naive Bayes dalam Klasifikasi Sentimen Twitter Terhadap Pelayanan BPJS Sistem Informasi , Fakultas Teknik dan Ilmu Komputer , Universitas Teknokrat Indonesia , Indonesia Comparison of SVM and Naive Bayes Alg. Jurnal Pendidikan Dan Teknologi Indonesia (JPTI), 5(3), 645–653.
Atimi, R. L., & Enda Esyudha Pratama. (2022). Implementasi Model Klasifikasi Sentimen Pada Review Produk Lazada Indonesia. Jurnal Sains Dan Informatika, 8(1), 88–96. https://doi.org/10.34128/jsi.v8i1.419
Damar Pratama, A. (2024). JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Journal homepage: https://jurnal.stkippgritulungagung.ac.id/index.php/jipi ANALISA SENTIMEN MASYARAKAT TERHADAP PENGGUNAAN CHATGPT MENGGUNAKAN METODE SUPPORT VECTOR MACHINE (SVM). Jurnal Ilmiah Penelitian Dan Pembelajaran Informatika, 9(1), 327–338. https://doi.org/10.29100/jipi.v9i1.4285
Dwi Jayanto, D. (2025). Moral Outrage , Sticky Crisis , dan Strategi Komunikasi Krisis Pertamina Studi Kasus Skandal Korupsi dan Pengoplosan BBM Dian Dwi Jayanto Patra Niaga , Riva Siahaan , serta sejumlah direksi anak usaha ( Tempo . co , 2025 ). Keterlibatan yang merusak reput. 13(1), 1–21.
Faisal, D., Berliana, F., Pahlevi, R., Febrian, R., Fathullah, D., Al-Ghozi, A., & Ramadhani, S. (2025). Dinamika Kepercayaan Publik di Media Sosial : Krisis Komunikasi Humas Terhadap Isu Korupsi Pertamina. Socius: Jurnal Penelitian Ilmu-Ilmu Sosial, 2(10), 362–372.
Fitri, S. D., Lestari, D., Bintana, R. R., & Aryani, R. (2024). Implementasi Model Support Vector Machine Dalam Analisa Sentimen Masyarakat Mengenai Kebijakan Penerapan Aplikasi Mypertamina Program Studi Sistem Informasi , Universitas Jambi , Indonesia Aplikasi MyPertamina merupakan aplikasi yang diluncurkan oleh PT P. Jurnal Publikasi Sistem Informasi Dan Telekomunikasi, 2(2), 176–193.
Hidayat, M. T. (2025). Analisis Sentimen Menggunakan Metode Support Vector Machine dengan Pengoptimalan Particle Swarm Optimization Muhammad Toyib Hidayat, Drs. Danardono, MPH., Ph.D. 0–1.
Ilham, A. (2023). Analisis Sentimen Masyarakat Terhadap Kesehatan Mental Pada Twitter Menggunakan Algoritme K-Nearest Neighbor. Prosiding Seminar Nasional Mahasiswa Fakultas …, 2(September), 539–547. http://senafti.budiluhur.ac.id/index.php/senafti/article/view/792%0Ahttp://senafti.budiluhur.ac.id/index.php/senafti/article/download/792/527
Liu, B. (2022). Sentiment Analysis and Opinion Mining (H. Graeme (ed.)). Springer International Publishing. https://books.google.co.id/books?id=xYhyEAAAQBAJ
Perhatian, D. (2024). ANALISIS SENTIMEN DAN PERILAKU PENGGUNA MEDIA SOSIAL TERHADAP ISU KESEHATAN MENTAL MENGGUNAKAN METODE NATURAL LANGUAGE PROCESSING ( NLP ) Analysis Of Sentiment And Behavior Of Social Media Users Towards Mental Health Issues Using The Natural Language Proc. 6(2), 153–158.
Rumaisa, F., Puspitarani, Y., Rosita, A., Zakiah, A., & Violina, S. (2021). Penerapan Natural Language Processing (NLP) di bidang pendidikan. Jurnal Inovasi Masyarakat, 1(3), 232–235. https://doi.org/10.33197/jim.vol1.iss3.2021.799
Sartika, D. (2020). Implementasi Algoritma K-Nearest Neighbour dalam Menganalisis Sentimen Terhadap Program Merdeka Belajar Kampus Merdeka ( MBKM ). 69–76.
Septiyanti, N. D., Luthfi, M. I., & Romadloni, N. T. (2024). Komparasi Metode Klasifikasi Dalam Analisis Sentimen Ulasan Pengguna Aplikasi KRL Access Di Google Play Store. Journal Computer Science and Information Systems : J-Cosys, 4(1), 64–75. https://doi.org/10.53514/jco.v4i1.495
Smith, B. G., & Gallicano, T. D. (2015). Terms of engagement: Analyzing public engagement with organizations through social media. Computers in Human Behavior, 53, 82–90. https://doi.org/https://doi.org/10.1016/j.chb.2015.05.060
Umam, K. (2024). MENGANALISIS RESPONS NETIZEN TWITTER TERHADAP PROGRAM MAKAN SIANG GRATIS MENERAPKAN NLP METODE NAÏVE BAYES. Jurnal Sistem Informasi, Teknologi Informasi Dan Komputer, 14(3), 201–208.
Wardaniah, S., Listia, H., Wulandari, S., Ramadhani, F., Dewi, S., Hasan, A., Studi, P., Komputer, I., Medan, N., Medan, K., Utara, P. S., Studi, P., Profesi, P., Tinggi, S., Kesehatan, I., Kabanjahe, A., Karo, K., & Utara, P. S. (2024). Analisis Sentimen Publik Terhadap Isu Pembatalan Revisi UU Pilkada 2024 dengan NLP. Jurnal Multidisiplin Indonesia, 3(2), 1367–1376.
Wukich, C. (2025). Social Media Engagement in Public Administration: Communication Goals That Influence Reactions, Comments, and Shares. Information Polity, 30(3), 165–179. https://doi.org/10.1177/15701255251363912
Zein, A., Farizy, S., & Suharyanto, E. (2022). Sentimen Analisis Pada Komentar Pendek Evaluasi Dosen Oleh Mahasiswa (Edom) Program Studi Sistem Informasi Universitas Pamulung. Jurnal Ilmu Komputer, 5(01), 17–23. https://jurnal.pranataindonesia.ac.id/index.php/jik/article/view/113%0Ahttps://jurnal.pranataindonesia.ac.id/index.php/jik/article/download/113/66
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Copyright (c) 2025 Devi Mustika, Lintang Mayzha Safira, Damayanti, Dwi Novaria Misidawati (Author)
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