Sentiment Analysis of Economic Policy Comments on YouTube Using Ensemble Machine Learning
DOI:
https://doi.org/10.30871/jaic.v9i5.10897Keywords:
Sentiment Analysis, Ensemble Learning, Youtube Comments, Economic Policy, Machine LearningAbstract
Public sentiment analysis of economic policies is increasingly important in the digital age, as social media platforms have become the main arena for public discussion. This study analyzes YouTube comments related to Tom Lembong's economic policies to address the lack of policy sentiment analysis tools in Indonesian. A dataset containing 1,029 comments was collected and systematically processed using normalization, stop word removal, and stemming techniques tailored to Indonesian. To overcome data scarcity and class imbalance, advanced data augmentation methods—synonym replacement, random insertion, and random deletion—were applied, expanding the dataset to 2,169 samples. Feature extraction used TF-IDF vectorization (unigram, bigram, trigram) and CountVectorizer, followed by an 80:20 split into training and testing sets. Several machine learning algorithms, including Support Vector Machine (SVM), Logistic Regression, Random Forest, Gradient Boosting, and Naïve Bayes, were evaluated with hyperparameter tuning through grid search. The results showed that SVM with TF-IDF bigrams achieved the best performance (accuracy: 96.08%, F1-score: 96.03%). Class-level evaluation showed high performance for negative sentiment (F1-score: 0.97) and positive sentiment (F1-score: 0.97), while neutral sentiment was more challenging (F1-score: 0.90) due to ambiguity, sarcasm, and fewer samples. The ensemble model, which combines several optimized SVM variants with soft voting, achieved robust and stable performance (accuracy and F1-score: 95.16%). These findings confirm the effectiveness of the ensemble-based approach for Indonesian sentiment analysis, while providing valuable insights into public perceptions of economic policy in the digital space.
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[1] Y. Waskithoaji, A. Darmawan, J. Manajemen, F. Bisnis, and D. Ekonomika, “Peran Teknologi dalam Penggunaan Media Sosial dan Dampaknya terhadap UMKM,” 2022. [Online]. Available: https://journal.uii.ac.id/selma/index
[2] K. Nurbagja, N. Saputra, A. Riyadi, and M. N. Tentua, “Sentiment Analysis of the Increase in Fuel Prices Using Random Forest Classifier Method,” Buletin Ilmiah Sarjana Teknik Elektro, vol. 5, no. 1, pp. 132–144, Mar. 2023, doi: 10.12928/biste.v5i1.7414.
[3] Ferdian Imawan, Diqy Fakhrun Shiddieq, and Fikri Fahru Roji, “Analisis Sentimen Publik di X Terhadap Rencana Kenaikan PPN 12% Menggunakan Bert,” CESS (Journal of Computer Engineering, System and Science), vol. 10, no. 1, pp. 136–148, Jan. 2025, doi: 10.24114/cess.v10i1.65884.
[4] BBC NEWS INDONESIA, “Kebijakan impor gula enam mendag era Jokowi – Apa yang terjadi saat Tom Lembong menjabat?,” BBC News Indonesia.
[5] F. Juma Pristika and F. Rozi, “Komunika: Jurnal Ilmu Komunikasi Sentimen Komentar Netizen dalam Postingan Pelantikan Menteri ATR/BPN pada Akun Instagram @agusyudhoyono,” vol. 11, 2024, doi: 10.22236/komunika.v11i2.15145.
[6] Tokoh.co.id, “Tom Lembong: Arsitek Kebijakan dan Reformasi Ekonomi Indonesia,” Tokoh.co.id. Accessed: Aug. 15, 2025. [Online]. Available: https://tokoh.co.id/tom-lembong-arsitek-kebijakan-ekonomi-indonesia/
[7] A. Y. Setiawan, I. Gede, M. Darmawiguna, and G. A. Pradnyana, “Sentiment Summarization Evaluasi Pembelajaran Menggunakan Algoritma Lstm (Long Short Term Memory),” Kumpulan Artikel Mahasiswa Pendidikan Teknik Informatika (KARMAPATI), vol. 11, no. 2, 2022.
[8] A. Kartika Sari, Akhmad Irsyad, Dinda Nur Aini, Islamiyah, and Stephanie Elfriede Ginting, “Analisis Sentimen Twitter Menggunakan Machine Learning untuk Identifikasi Konten Negatif,” Adopsi Teknologi dan Sistem Informasi (ATASI), vol. 3, no. 1, pp. 64–73, Jun. 2024, doi: 10.30872/atasi.v3i1.1373.
[9] O. N. Cahyani and F. Budiman, “Performa Logistic Regression dan Naive Bayes dalam Klasifikasi Berita Hoax di Indonesia,” Edumatic: Jurnal Pendidikan Informatika, vol. 9, no. 1, pp. 60–68, Apr. 2025, doi: 10.29408/edumatic.v9i1.28987.
[10] Y. A. Mustofa, I. Surya, and K. Idris, “Pendekatan Ensemble pada Analisis Sentimen Ulasan Aplikasi Google Play Store Ensemble Approach to Sentiment Analysis of Google Play Store App Reviews,” Jambura Journal of Electrical and Electronics Engineering, vol. 6 nomor 3, Jul. 2024.
[11] A. Mohammed and R. Kora, “An effective ensemble deep learning framework for text classification,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 10, pp. 8825–8837, Nov. 2022, doi: 10.1016/j.jksuci.2021.11.001.
[12] E. Daniati and H. Utama, “Analisis Sentimen Dengan Pendekatan Ensemble Learning Dan Word Embedding Pada Twitter,” 2023.
[13] M. I. Fikri, T. S. Sabrila, and Y. Azhar, “Perbandingan Metode Naïve Bayes dan Support Vector Machine pada Analisis Sentimen Twitter,” SMATIKA JURNAL, vol. 10, no. 02, pp. 71–76, Dec. 2020, doi: 10.32664/smatika.v10i02.455.
[14] M. U. Albab, Y. K. P., and M. N. Fawaiq, “Optimization of the Stemming Technique on Text Preprocessing President 3 Periods Topic,” Jurnal Transformatika, vol. 20, no. 2, pp. 1–12, Jan. 2023, doi: 10.26623/transformatika.v20i2.5374.
[15] A. A. Khan, O. Chaudhari, and R. Chandra, “A review of ensemble learning and data augmentation models for class imbalanced problems: Combination, implementation and evaluation,” Expert Syst Appl, vol. 244, p. 122778, Jun. 2024, doi: 10.1016/j.eswa.2023.122778.
[16] H.-N. Huang et al., “Employing feature engineering strategies to improve the performance of machine learning algorithms on echocardiogram dataset,” Digit Health, vol. 9, Jan. 2023, doi: 10.1177/20552076231207589.
[17] K. Shah, H. Patel, D. Sanghvi, and M. Shah, “A Comparative Analysis of Logistic Regression, Random Forest and KNN Models for the Text Classification,” Augmented Human Research, vol. 5, no. 1, p. 12, Dec. 2020, doi: 10.1007/s41133-020-00032-0.
[18] D. Pradana and E. Sugiharti, “Implementation Data Mining with Naive Bayes Classifier Method and Laplace Smoothing to Predict Students Learning Results,” Recursive Journal of Informatics, vol. 1, no. 1, pp. 1–8, Mar. 2023, doi: 10.15294/rji.v1i1.63964.
[19] R. I. Arumnisaa and A. W. Wijayanto, “SISTEMASI: Jurnal Sistem Informasi Perbandingan Metode Ensemble Learning: Random Forest, Support Vector Machine, AdaBoost pada Klasifikasi Indeks Pembangunan Manusia (IPM) Comparison of Ensemble Learning Method: Random Forest, Support Vector Machine, AdaBoost for Classification Human Development Index (HDI).” [Online]. Available: http://sistemasi.ftik.unisi.ac.id
[20] I. G. T. Isa and F. Elfaladonna, “Penilaian Kinerja Akurasi Metode Klasifikasi dalam Dataset Penerimaan Mahasiswa Baru Universitas XYZ,” Jurnal Edukasi dan Penelitian Informatika (JEPIN), vol. 8, no. 2, p. 292, Aug. 2022, doi: 10.26418/jp.v8i2.54316.
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