Comparison of SVM and Random Forest for TikTok E10 Fuel Sentiment Analysis
DOI:
https://doi.org/10.30871/jaic.v10i2.12395Keywords:
E10 Fuel, Random Forest, Sentiment Analysis, Support Vector Machine, TikTokAbstract
One source of data that can be used to gauge public opinion on a specific public policy is social media. This study is to examine public opinion regarding the policy on the usage of a 10% bioethanol mix (E10) based on user comments on the TikTok platform. The sentiment analysis approach uses two classification algorithms Random Forest (RF) and Support Vector Machine (SVM). Pretreatment stages of data processing include tokenization, stemming, and lexicon-based techniques for identifying sentiment polarity. The Term Frequency–Inverse Document Frequency (TF-IDF) approach is used to extract features. The Synthetic Minority Over-sampling (SMOTE) technique was used to address class distribution imbalance in the data. Based on the test results, the accuracy achieved by the SVM and RF algorithms was 82.19% before applying SMOTE. Accuracy increased to 89.55% for SVM and 89.59% for RF after data balancing with SMOTE. Additionally, there was a more consistent improvement in precision, recall, and F1-score values. The findings of this study indicate that the use of SMOTE can improve the performance of classification models and reduce bias caused by class imbalance in sentiment analysis related to the E10 policy on social media.
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[1] D. Tewu, D. Destine, I. Gunawan, and I. M. A. Y. June, “Analysis of Social Media User Growth and Its Implications for Digital Marketing Strategies in Indonesia 2024,” pp. 236–245, 2025.
[2] A. H. Sebayang, H. Ibrahim, S. Dharma, A. S. Silitonga, B. B. Ginting, and N. Damanik, “Pengaruh Campuran Bahan Bakar Pertalite-Bioetanol Biji Sorghum pada Mesin Bensin,” J. Teknosains, vol. 9, no. 2, p. 91, Jul. 2020, doi: 10.22146/teknosains.40502.
[3] A. D. Nugroho, M. S. Alim, S. Sundari, and G. R. Soekarno, “Kebijakan Dekarbonisasi Sistem Energi Indonesia pada Sektor Energi Terbarukan,” CAKRAWALA, vol. 17, no. 2, pp. 109–125, Dec. 2023, doi: 10.32781/cakrawala.v17i2.539.
[4] A. Yoga Pratama, G. Ananda Sanjaya, N. Khairunisa Lubis, and M. Rangga Aditya, “Analisis Sentimen Publik Terkait Danantara Menggunakan Algoritma IndoBERT pada Platform Media Sosial,” vol. 9, p. 2025, doi: 10.47002/metik.v9i1.1055.
[5] A. Rustanta, S. Dwi Putranto, and P. Huang, “Maintaining the Digital Public Space: Communication Ethics and Regulatory Challenges in the TikTok Era,” J. Komun., vol. 17, no. 1, pp. 63–83, 2025, doi: 10.24912/jk.v17i1.32927.
[6] M. Joefitra Zaqy, L. Marlina, and R. F. Wijaya, “Analysis of Indonesian Netizen Sentiment on Platform X Regarding the Arrival of Refugees in Indonesia Using the Multinominal Naive Bayes Method,” sinkron, vol. 8, no. 3, pp. 1945–1952, Jul. 2024, doi: 10.33395/sinkron.v8i3.13940.
[7] L. F. S. Minggow, A. V. Vitianingsih, S. Kacung, A. L. Maukar, and J. F. Rusdi, “Sentiment Analysis on Ajaib App Using the SVM Method,” J. Sisfokom (Sistem Inf. dan Komputer), vol. 14, no. 4, pp. 551–556, Oct. 2025, doi: 10.32736/sisfokom.v14i4.2402.
[8] R. Yuranda, T. Sutabri, and D. Wahyuningsih, “Pendekatan Macine Learning dalam Evaluasi LabelBerita Berdasarkan Judul: Studi Kasus Media Online,” J. Sisfokom (Sistem Inf. dan Komputer), vol. 12, no. 3, pp. 434–439, 2023.
[9] L. B. Ilmawan and M. A. Mude, “Perbandingan Metode Klasifikasi Support Vector Machine dan Naïve Bayes untuk Analisis Sentimen pada Ulasan Tekstual di Google Play Store,” Ilk. J. Ilm., vol. 12, no. 2, pp. 154–161, Aug. 2020, doi: 10.33096/ilkom.v12i2.597.154-161.
[10] R. T. Aldisa and P. Maulana, “Analisis Sentimen Opini Masyarakat Terhadap Vaksinasi Booster COVID-19 Dengan Perbandingan Metode Naive Bayes, Decision Tree dan SVM,” Build. Informatics, Technol. Sci., vol. 4, no. 1, pp. 106–109, Jun. 2022, doi: 10.47065/bits.v4i1.1581.
[11] G. R. Putri, M. A. Maulana, and S. Bahri, “Perbandingan Algoritma Naïve Bayes dan TextBlob Untuk Mendapatkan Analisis Sentimen Masyarakat Pada Sosial Media,” Teknika, vol. 13, no. 2, pp. 213–218, Jun. 2024, doi: 10.34148/teknika.v13i2.815.
[12] P. Insan and Kusrini, “Analisis Perbandingan Algoritma ID3 dan KNN Pada Klasifikasi Emosi Teks Berita Berbahasa Indonesia,” METIK J., vol. 5, no. 1, pp. 36–41, Jun. 2021, doi: 10.47002/metik.v5i1.213.
[13] A. Tirta, P. Subandono, and D. Ariatmanto, “Optimalisasi Seleksi Fitur dalam Analisis Sentimen Bank Saqu: Studi Perbandingan SVM dan Random Forest Menggunakan Information Gain dan Chi-Square Optimizing Feature Selection in Sentiment Analysis of Bank Saqu: A Comparative Study of SVM and Random Fores,” Sist. J. Sist. Inf., vol. 14, pp. 1205–1219, 2025, [Online]. Available: http://sistemasi.ftik.unisi.ac.id
[14] Y. Afandy, “Perbandingan SVM dan Random Forest Pada Analisis Sentimen Kebijakan Tabungan Perumahan Rakyat Berdasarkan Data Media Sosial X,” vol. x, No.x, no. 2, pp. 28–36.
[15] F. Naifah Firzatullah, “Analisis Sentimen Pengguna Aplikasi Byond BSI Pada Google Play Store Menggunakan Algoritma SVM Dan Random Forest,” vol. 9, p. 2025, doi: 10.47002/metik.v9i2.1089.
[16] C. Z. V. Junus, T. Tarno, and P. Kartikasari, “Klasifikasi Menggunakan Metode Support Vector Machine Dan Random Forest Untuk Deteksi Awal Risiko Diabetes Melitus,” J. Gaussian, vol. 11, no. 3, pp. 386–396, Jan. 2023, doi: 10.14710/j.gauss.11.3.386-396.
[17] R. Astri, A. Kamal, and U. Dharma Andalas, “It-Dashboard Application To Determine the Type of Subsidized Assistance,” Res. Technol. Repub. Indones., vol. 17, no. 3, p. 2023, 2023, [Online]. Available: https://creativecommons.org/licenses/by/4.0/%0Ahttps://sinta3.kemdikbud.go.id/journals/profile/2143
[18] N. Adila, “Implementation of Web Scraping for Journal Data Collection on the SINTA Website,” Sinkron, vol. 7, no. 4, pp. 2478–2485, 2022, doi: 10.33395/sinkron.v7i4.11576.
[19] S. Chaichulee, C. Promchai, T. Kaewkomon, C. Kongkamol, T. Ingviya, and P. Sangsupawanich, “Multi-label classification of symptom terms from free-text bilingual adverse drug reaction reports using natural language processing,” PLoS One, vol. 17, no. 8 August, pp. 1–22, 2022, doi: 10.1371/journal.pone.0270595.
[20] A. N. Syafia, M. F. Hidayattullah, and W. Suteddy, “Studi Komparasi Algoritma SVM Dan Random Forest Pada Analisis Sentimen Komentar Youtube BTS,” vol. 8, no. 3, 2023.
[21] T. Informatika and U. Dian, “Comparing Machine Learning Models for Sentiment Analysis of Tokopedia Reviews,” vol. 9, no. 6, pp. 3642–3647, 2025.
[22] C. C. Sujadi, Y. Sibaroni, and A. F. Ihsan, “Analysis Content Type and Emotion of the Presidential Election Users Tweets using Agglomerative Hierarchical Clustering,” Sinkron, vol. 8, no. 3, pp. 1230–1237, 2023, doi: 10.33395/sinkron.v8i3.12616.
[23] S. D. Amalia, M. A. Barata, and P. E. Yuwita, “Optimization of Random Forest Algorithm with Backward Elimination Method in Classification of Academic Stress Levels,” J. Appl. Informatics Comput., vol. 9, no. 3, pp. 633–641, 2025, doi: 10.30871/jaic.v9i3.9280.
[24] Bakti Putra Pamungkas, Muhammad Jauhar Vikri, and Ita Aristia Sa’ida, “Application of SMOTE-ENN Method in Data Balancing for Classification of Diabetes Health Indicators with C4.5 Algorithm,” J. Sisfokom (Sistem Inf. dan Komputer), vol. 14, no. 2, pp. 183–188, 2025, doi: 10.32736/sisfokom.v14i2.2350.
[25] M. Mujahid et al., “Data oversampling and imbalanced datasets: an investigation of performance for machine learning and feature engineering,” J. Big Data, vol. 11, no. 1, 2024, doi: 10.1186/s40537-024-00943-4.
[26] M. Fachrie, “Machine Learning for Data Classification in Indonesia Regional Elections Based on Political Parties Support,” J. Ilmu Komput. dan Inf., vol. 13, no. 2, pp. 89–96, 2020, doi: 10.21609/jiki.v13i2.860.
[27] E. F. Laili, Z. Alawi, R. Rohmah, and A. Barata, “Komparasi Algoritma Decision Tree Dan Support Vector Machine (SVM) Dalam Klasifikasi Serangan Jantung,” J. Sist. Inf. dan Inform., vol. 8, no. 1, 2025, [Online]. Available: https://www.kaggle.com/datasets/thxogg/heart-
[28] S. Dermawan, A. T. Ayunda, S. Informasi, F. Sains, and U. Pradita, “Sentiment Analysis of Coretax on Social Media X Using Naive Bayes , SVM , and LSTM for Service Improvement,” vol. 9, no. 6, 2025.
[29] A. A. Yaqin, M. A. Barata, and N. Mahmudah, “Implementation of the Random Forest Algorithm with Optuna Optimization in Lung Cancer Classification,” Sistemasi, vol. 14, no. 2, p. 561, 2025, doi: 10.32520/stmsi.v14i2.4877.
[30] W. A. Rayadhani and M. Rahardi, “Comparative Analysis of Random Forest , SVM , and Naive Bayes for Cardiovascular Disease Prediction,” vol. 9, no. 6, pp. 3234–3243, 2025.
[31] R. A. Sitorus and I. Zufria, “Application of the Naïve Bayes Algorithm in Sentiment Analysis of Using the Shopee Application on the Play Store,” Digit. Zo. J. Teknol. Inf. dan Komun., vol. 15, no. 1, pp. 53–66, May 2024, doi: 10.31849/digitalzone.v15i1.19828.
[32] A. A. Ritonga, A. Amanda, and E. R. Hasibuan, “Predicting Prospective Student Interests Using the C4.5 Algorithm and Naive Bayes,” Sinkron, vol. 9, no. 1, pp. 395–405, 2025, doi: 10.33395/sinkron.v9i1.14441.
[33] Darussalam and G. Arief, “Jurnal Resti,” Resti, vol. 1, no. 1, pp. 19–25, 2018.
[34] Y. Aprianti, A. L. Hananto, and S. S. Hilabi, “Klasifikasi Sentimen Komentar Pengguna pada Aplikasi Ruangguru Menggunakan Algoritma Naive Bayes Ruangguru menunjukkan inovasi dalam pendekatan Naïve Bayes menjadi alat klasifikasi teks yang populer . Penerapan Penelitian lain oleh Artanti Inez TF-IDF pen,” pp. 101–110, 2025, doi: 10.47002/metik.v9i1.1023.
[35] K. Pal, “and Holdout Accuracy Estimation Methods with 5,” no. Iccmc, pp. 83–87, 2020.
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