Analisis Sentimen X Terhadap Isu Industri Sawit Prabowo Subianto Menggunakan TF-IDF dan Machine Learning
- Ibrahim Akbar Arga Dewangga Universitas Dian Nuswantoro, Semarang, Indonesia
-
Rama Aria Megantara
*
Universitas Dian Nuswantoro, Semarang,
Indonesia
(*) Corresponding Author
Abstract
This study aims to analyze public sentiment on the X platform regarding the palm oil industry issue associated with Prabowo Subianto and to compare the performance of Decision Tree, Support Vector Machine (SVM), and Random Forest algorithms. The dataset consisted of 3,785 tweets collected through a crawling process. The data were then processed through cleaning, case folding, text normalization, tokenizing, stopword removal, and stemming. Sentiment labeling was conducted using a lexicon-based approach, followed by feature extraction using Term Frequency-Inverse Document Frequency (TF-IDF) and traintest data splitting. The labeling results show that public opinion was dominated by positive sentiment with 3,018 tweets (79.7%), while negative sentiment accounted for 767 tweets (20.3%). The experimental results indicate that SVM achieved the best performance with an accuracy of 0.90, followed by Random Forest with 0.86 and Decision Tree with 0.84. SVM also demonstrated more stable performance based on precision, recall, and F1-score across both sentiment classes. These findings indicate that SVM is the most effective model for Indonesian-language sentiment classification on palm oil policy issues and has strong potential to support public policy evaluation based on social media data.
Downloads
References
M. Ma’rufudin and A. Yudhistira, “Analisis Sentimen Petani Milenial Pada Media Sosial X Menggunakan Algortitma Support Vector Machine (SVM),” J. Pendidik. dan Teknol. Indones., vol. 5, no. 3, pp. 845–857, 2025, doi: 10.52436/1.jpti.717.
A. Ma’ruf, A. E. Pajri, and P. Liana, “Sentiment Analysis of President Prabowo’s Performance on Twitter (X) with a Comparative Study of SVM, XGBoost, and AdaBoost,” Journal of Applied Informatics and Computing (JAIC), vol. 10, no. 1, pp. 684–697, Feb. 2026, doi: 10.30871/jaic.v10i1.12138.
T. Wahyudi, Rudiman, and N. A. Verdikha, “Klasifikasi sentimen X-Twitter perihal pemindahan ibu kota Indonesia menggunakan ekstraksi fitur TF-IDF dan metode Support Vector Machine (SVM),” Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika, vol. 18, no. 2, pp. 185–199, 2024, doi: 10.47111/jti.v18i2.15015.
N. Nur, A. Aryanti, and O. Suria, "Analisis sentimen terhadap pemutusan hubungan kerja di Indonesia: Komparasi IndoBERT dengan SVM, Random Forest, dan Decision Tree dengan optimasi TF-IDF," Building of Informatics, Technology and Science (BITS), vol. 10, no. 2, pp. 1158–1176, 2025, doi: 10.47065/bits.v10i2.999.
V. B. Lestari and C. A. Hutagalung, “Evaluation of TF-IDF ExtractionTechniques in Sentiment Analysis ofIndonesianLanguage MarketplacesUsing SVM, Logistic Regression, andNaive Bayes,” J. Comput. Sci. Appl., vol. 8, no. 1, pp. 22– 2025, 2025, [Online]. Available: https://doi.org/10.21009/j-
M. N. R. N. Suryono, A. A. Arifiyanti, and D. S. Y. Kartika, “Aspect-based sentiment analysis pada ulasan aplikasi Access by KAI menggunakan metode TF-IDF dan algoritma Support Vector Machine,” Jurnal INSTEK: Informatika Sains dan Teknologi, vol. 10, no. 2, pp. 414–423, Oct. 2025, doi: 10.24252/instek.v10i2.57155.
R. D. Cahyani and P. T. Prasetyaningrum, “Sentiment Analysis of User Reviews for AI Applications : Evaluating SVM , Logistic Regression , and Random Forest,” vol. 8, no. 1, pp. 1–27, 2026, doi: 10.63158/journalisi.v8i1.1366.
U. A. Pringsewu, S. Pahrullah, D. Feriyanto, T. H. Andika, and N. Aminudin, “Perbandingan Dan Efektivitas Kinerja Algoritma Svm Dan Bi-Lstm Dengan Tf-Idf Dan Smote Untuk Analisis Sentimen Kelestarian Hewan Liar,” vol. 7, no. 1, pp. 9–14.
J. E. Br Sinulingga and H. C. K. Sitorus, “Analisis Sentimen Opini Masyarakat terhadap Film Horor Indonesia Menggunakan Metode SVM dan TF-IDF,” J. Manaj. Inform., vol. 14, no. 1, pp. 42–53, 2024, doi: 10.34010/jamika.v14i1.11946.
Indriani and A. Davy Wiranata, “Perbandingan Tingkat Akurasi Algoritma Svm, Decision Tree,Dan Random Forest Dalam Analisis Sentimen Tanggapan Pengguna Aplikasi Gopay,” J. Tek. Inform., vol. 5, no. 3, pp. 777–787, 2024, [Online]. Available: https://doi.org/10.52436/1.jutif.2024.5.3.1885
B. M. Obidje and M. A. I. Pakereng, “Analisis Sentimen Pemilihan Presiden dan Wakil Presiden Tahun 2024 Di Twitter Menggunakan Metode Klasifikasi Naive Bayes,” JIPI (Jurnal Ilm. Penelit. dan Pembelajaran Inform., vol. 10, no. 1, pp. 424–433, 2025, doi: 10.29100/jipi.v10i1.5836.
M. R. Saputra and P. Parjito, “Analisis Sentimen Twitter Terhadap Konflik Di Papua Menggunakan Perbandingan Naive Bayes Dan Svm,” JIPI (Jurnal Ilm. Penelit. dan Pembelajaran Inform., vol. 10, no. 2, pp. 1197–1208, 2025, doi: 10.29100/jipi.v10i2.6180.
A. B. Aditya, S. Samsudin, W. P. Rizki, M. Mahendra, and A. Setiawan, “Analisis Sentimen Ulasan Aplikasi Gojek Menggunakan Support Vector Machine Dan Random Forest,” J. Inform. Terpadu, vol. 11, no. 2, pp. 134–143, 2025, doi: 10.54914/jit.v11i2.1884.
V. A. Sulistiani and M. Hamka, “Analisis Sentimen Pengguna Media Sosial Terhadap Identitas Kependudukan Digital Menggunakan Metode Support Vector Machine (SVM),” J. Inf. Syst. Res., vol. 5, no. 4, pp. 1323–1332, 2024, doi: 10.47065/josh.v5i4.5614.
F. Diannita Sari, W. Widayat, and R. Artikel, “Analisis sentimen masyarakat terhadap PON 2024 Aceh dan Sumatra Utara di Twitter menggunakan algoritma SVM dan KNN,” AITI J. Teknol. Inf., vol. 22, no. 2, pp. 102–116, 2025.
R. F. Apriyani, D. A. Megawaty, S. Informasi, U. T. Indonesia, B. Lampung, and K. Merdeka, “Komparasi Performa Klasifikasi Sentimen Masyarakat Terhadap Kurikulum Merdeka di Sekolah Menggunakan SVM dan KNN,” vol. 6, no. 4, pp. 2795–2806, 2025, doi: 10.47065/bits.v6i4.6877.
E. T. Pardede, K. D. Tania, and M. Afrina, “Knowledge Discovery: Analisis Sentimen dan Emosi WhatsApp Business dengan Machine learning dan Deep Learning,” J. Nas. Teknol. dan Sist. Inf., vol. 11, no. 3, pp. 310–318, 2026, doi: 10.25077.
A. Mustika Rani and N. Hendrastuty, “Perbandingan Algoritma NBC Dan SVM Untuk Melakukan Analisis Sentimen Terhadap PP NO.82 Tahun 2021,” Technol. Sci., vol. 6, no. 4, pp. 2139–2151, 2025, doi: 10.47065/bits.v6i4.6496.
N. C. Ramadani, I. Tahyudin, and A. Shouni Barkah, “Perbandingan Algoritma Support Vector Machine, Decision Tree, dan Logistic Regresion Pada Analisis Sentimen Ulasan Aplikasi Netflix,” J. Nas. Teknol. dan Sist. Inf., vol. 10, no. 2, pp. 110–117, 2024, doi: 10.25077/teknosi.v10i2.2024.110-117.
P. Publik, S. Twitter, and J. Juliantono, “Persepsi Publik Terhadap Kepemimpinan Firli Bahuri di KPK: Pendekatan Sentimen Twitter dengan Naïve Bayes dan SVM,” JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), vol. 10, no. 2, pp. 1272–1285, 2025. [Online]. Available: https://doi.org/10.29100/jipi.v10i2.6181.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Analisis Sentimen X Terhadap Isu Industri Sawit Prabowo Subianto Menggunakan TF-IDF dan Machine Learning
Pages: 39-50
Copyright (c) 2026 Ibrahim Akbar Arga Dewangga, Rama Aria Megantara
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
