OPINI PUBLIK TERHADAP ULASAN VIDEO RUU TNI MENGGUNAKAN TF-IDF, NAÏVE BAYES DAN SMOTE
DOI:
https://doi.org/10.36002/jutik.v12i1.3955Keywords:
Naïve bayes, public opinion, RUU TNI, SMOTE, TF-IDFAbstract
The rapid development of digital technology has encouraged the public to actively express their opinions on public issues through social media platforms, including YouTube. The comment section on videos discussing the Draft Law on the Indonesian National Armed Forces (RUU TNI) has become a space for the public to convey support or rejection. This study aims to analyze public opinion regarding the RUU TNI by classifying YouTube comments into two sentiment categories: positive and negative. The methods employed include text preprocessing, feature extraction using TF-IDF, sentiment classification with the Naïve Bayes algorithm, and data balancing using the SMOTE technique to address class imbalance. The evaluation results show that the model achieved 80.7% accuracy before SMOTE; however, the recall and f1-score for the positive class were very low due to the imbalanced dataset. After applying SMOTE, the accuracy slightly decreased to 80.38%, but there was a significant improvement in the evaluation metrics for the positive class, with recall reaching 86.21% and f1-score 81.3%. WordCloud visualization also revealed dominant keywords that represent each sentiment. These findings indicate that the Naïve Bayes algorithm, when combined with SMOTE, is more effective in producing a balanced sentiment classification and is recommended for use in analyzing imbalanced textual data related to public opinion.
References
[1] N. Annisa Murnastiti dan T. Noor Fatyanosa, “Analisis Sentimen Terhadap Makanan Manis di Platform X Menggunakan TF-IDF dan Naive Bayes,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 1, no. 1, hlm. 2548–964, 2025, [Daring]. Tersedia pada: http://j-ptiik.ub.ac.id
[2] A. Prayoga Siswono dkk., “Analisis Sentimen Pelantikan Presiden Indonesia 2024 Menggunakan Model Klasifikasi dan Algoritma Naive Bayes,” CENTIVE, vol. 4, no. 1, hlm. 1024–1035, 2024.
[3] R. N. Rahmawaty, A. Pambudi, U. M. Sukabumi, K. Sukabumi, dan J. Barat, “Penerapan Metode Naïve Bayes dan Cosine Similarity Dalam Analisis Sentimen Terhadap Platform Film Ilegal di Media Sosial X (Twiter),” JAMASTIKA, vol. 3, 2024, doi:10.35473/jamastika.v3i1.3059
[4] T. Prana, W. Sukma, dan M. R. Pribadi, “Analisis Sentimen Review Pengguna Viu pada Play Store dengan Algoritma Random Forest,” Journal Of Software Engineering And Computational Intelligence, vol. 2, no. 1, 2024. doi: 10.36982/jseci.v2i01.4016
[5] R. Y. Hidayat, “Analisis Dan Klasifikasi Sentimen Terhadap Brand Infinix Tecno dan Itel Menggunakan Kombinasi Metode Naive Bayes Dan Cosine Similarity,” JAMASTIKA, vol. 4, 2025. doi: 10.35473/jamastika.v4i1.368
[6] P. Ganda Dewata, A. Rizky, dan H. Irsyad, “JURNAL REIN (REKAYASA INFORMATIKA) Analisis Sentimen Terhadap Boikot Produk Israel Menggunakan Algoritma Naive Bayes Dan SMOTE,” Jurnal Rekayasa Informatika, vol. 1, no. 1, hlm. 7–15, 2024.
[7] I. P. Ramayasa, I. G. A. D. Saryanti, I. K. Dharmendra, dan Edwar, “Perbandingan Metode Vektorisasi Pada Analisa Sentiment, Studi Kasus: Cyberbullying Pada Komentar Instagram,,” Jurnal Teknologi Informasi dan Komputer, vol. 9, no. 5, Okt 2023, doi: 10.36002/JUTIK.V9I5.2645.
[8] F. Fathoni, A. Ibrahim, F. R. Mumtaz, M. A. Zaky, M. J. Pratama, dan I. A. Kurniawan, “Analisis Sentimen Public Twitter Terhadap Kebijakan Pemerintah Menggunakan Metode SVM (Studi Kasus: RUU TNI)”JATI (Jurnal Mahasiswa Teknik Informatika), vol. 9, no. 4, hlm. 6322–6329, Mei 2025, doi: 10.36040/JATI.V9I4.14036.
[9] J. E. Br Sinulingga dan H. C. K. Sitorus, “Analisis Sentimen Opini Masyarakat terhadap Film Horor Indonesia Menggunakan Metode SVM dan TF-IDF,” Jurnal Manajemen Informatika (JAMIKA), vol. 14, no. 1, hlm. 42–53, Feb 2024, doi: 10.34010/jamika.v14i1.11946.
[10] F. Destiyanti, A. Id Hadiana, F. Rakhmat Umbara, dan U. Jenderal Achmad Yani Jl Terusan Jenderal Jenderal Sudirman, “Penerapan Metode Support Vector Machine dan SMOTE untuk Klasifikasi Sentimen Publik Terhadap Polisi Republik Indonesia,” Jurnal Masyarakat Informatika Unjani, vol. 8, no. 1, hlm. 1–15, 2024. doi: 10.25077/teknosi.v9i2.2023.163-172.
[11] A. F. Anjani, D. Anggraeni, dan I. M. Tirta, “Implementasi Random Forest Menggunakan SMOTE untuk Analisis Sentimen Ulasan Aplikasi Sister for Students UNEJ,” Jurnal Nasional Teknologi dan Sistem Informasi, vol. 9, no. 2, hlm. 163–172, Sep 2023, doi: 10.25077/teknosi.v9i2.2023.163-172.
[12] V. Amalia Herlinda, C. Sri Kusuma Aditya, dan D. Rizki Chandranegara, “Analisis Sentimen Masyarakat Terhadap Generasi Z Dalam Dunia Kerja Pada Media Sosial Twitter Menggunakan Metode Naïve Bayes,” REPOSITOR, vol. 6, no. 4, hlm. 405–414, 2024.
[13] T. Ramadha Triputra dan A. Rubhasy, “Analisis Sentimen Ulasan Pengguna Aplikasi Facebook Di Google Play Store Menggunakan Algoritma Naïve Bayes Dan K-Nearest Neighbor,Jurnal Mahasiswa Teknik Informatika, vol. 9, no. 3, 2025.
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