Analisis Sentimen: Perbandingan Performa Algoritma Naive Bayes, Support Vector Machine, Random Forest, dan K-Nearest Neighbor Dalam Pemecatan Shin Tae Yong pada Media X
- Agung Prasatya Universitas Teknokrat Indonesia, Bandar Lampung, Indonesia
-
Nirwana Hendrastuty
*
Universitas Teknokrat Indonesia, Bandar Lampung,
Indonesia
(*) Corresponding Author
Abstract
The dismissal of Shin Tae Yong as the coach of the Indonesian national team has triggered a variety of reactions, ranging from disappointment to relief, among Indonesian football fans. Factors such as unsatisfactory match results and internal conflicts within the team, as well as pressure from fans and the media, were the main reasons for this decision. Although this change opens up opportunities for a new coach to improve the performance of the Indonesian national team, it also raises controversy and debate. This study aims to compare the performance of Naïve Bayes, Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) algorithms in analyzing sentiment related to this dismissal. The research data were obtained from the Twitter platform with a total of 4,345 tweets collected using crawling techniques. The data then underwent pre-processing stages to produce clean data. Testing was conducted to evaluate the accuracy of each model in predicting public sentiment. The test results showed that the SVM algorithm performed best with an accuracy of 78%, followed by Random Forest with an accuracy of 77%, and Naïve Bayes with an accuracy of 63% and KNN 74% before the application of Synthetic Minority Oversampling Technique (SMOTE). After optimization using SMOTE, the SVM algorithm still showed the best performance with an accuracy of 80%, followed by Random Forest with an accuracy of 79%, and Naïve Bayes and KNN with an accuracy of 72%. Based on these results, SVM proved to be the most effective algorithm in classifying sentiment related to the dismissal of Shin Tae Yong. It is hoped that the results of this study can contribute to understanding public opinion regarding the decision to dismiss Shin Tae Yong as coach of the Indonesian national team.
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References
R. R. Pratama and R. R. Suryono, “Performance Comparison Of Naive Bayes, Support Vector Machine And Random Forest Algorithms For Apple Vision Pro Sentiment Analysis,” J. Tek. Inform. Vol., vol. 6, no. 1, pp. 31–39, 2025, doi: https://doi.org/10.52436/1.jutif.2025.6.1.4035.
T. A. Anastasya, A. Diani, P. Saka, M. Juventus, D. Deke, and A. M. Rizki, “Optimasi Algoritma Svm Dengan Pso Untuk Analisis Sentimen Pada Media Sosial X Terhadap Kinerja Timnas Di Era Shin Tae-Yong,” (Jurnal Mhs. Tek. Inform., vol. 9, no. 1, pp. 384–391, 2025, doi: https://doi.org/10.36040/jati.v9i1.12298.
H. F. Wibowo and D. Nugroho, “Analisis Momen Pola Menyerang, Bertahan dan Transisi pada Timnas Indonesia U-19 Vs Bulgaria,” J. Sport Sci. Educ. |, vol. 6, no. 1, p. 57, 2021, [Online]. Available: http://journal.unesa.ac.id/index.php/jossae/indexhttps://doi.org/10.26740/jossae.v6n2
S. Imaddudin, I. Astuti, and S. Ruhama, “Studi Sentimen Masyarakat terhadap PSSI di Era Erick Thohir menggunakan Algoritma Support Vector Machine ( SVM ) pada Media Sosial X,” J. Penelit. MULTIDISIPLIN BANGSA, vol. 1, no. 8, pp. 1003–1013, 2025, doi: https://doi.org/10.59837/jpnmb.v1i8.193.
A. C. M. Pratama and A. T. Laksana, “Lahirnya kembali PSSI (Persatuan Sepak Bola Seluruh Indonesia) Tahun 1950-1954,” AVATARA, e-Journal Pendidik. Sej., vol. 10, no. 1, pp. 1–9, 2020
S. K. Fadhillah and Y. S. Isma, “Analisis Komentar Netizen di Instagram pada Pemberitaan Pemecatan Shin Tae-Yong Universitas Malikussaleh , Indonesia Universitas Malikussaleh , Indonesia Analisis Komentar Netizen di Instagram pada Pemberitaan Pemecatan Shin Tae-Yong PSSI untuk melatih Ti,” TUTURAN J. Ilmu Komunikasi, Sos. dan Hum., vol. 3, no. 1, p. 205, 2025, doi: https://doi.org/10.47861/tuturan.v3i1.1566.
J. A. Septian, T. M. Fachrudin, and A. Nugroho, “Analisis Sentimen Pengguna Twitter Terhadap Polemik Persepakbolaan Indonesia Menggunakan Pembobotan TF-IDF dan K-Nearest Neighbor,” J. Intell. Syst. Comput., vol. 1, no. 1, pp. 43–49, 2019, doi: 10.52985/insyst.v1i1.36.
D. R. P. Jaya and S. Lestari, “Analisis Sentimen Naturalisasi Tim Nasional Indonesia U-23 di Era Shin Tae-yong Menggunakan Algoritma Naïve Bayes,” J. Indones. Manaj. Inform. dan Komun., vol. 5, no. 3, pp. 3262–3277, 2024, [Online]. Available: https://journal.stmiki.ac.id/index.php/jimik/
F. S. Pamungkas and I. Kharisudin, “Analisis Sentimen dengan SVM, NAIVE BAYES dan KNN untuk Studi Tanggapan Masyarakat Indonesia Terhadap Pandemi Covid-19 pada Media Sosial Twitter,” Pros. Semin. Nas. Mat., vol. 4, no. 628–634, pp. 1–7, 2021, [Online]. Available: https://journal.unnes.ac.id/sju/prisma/article/view/45038
F. A. Larasati, D. E. Ratnawati, and B. T. Hanggara, “Analisis Sentimen Ulasan Aplikasi Dana dengan Metode Random Forest,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 6, no. 9, pp. 4305–4313, 2022, [Online]. Available: https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/11562
A. Baita, Y. Pristyanto, and N. Cahyono, “Analisis Sentimen Mengenai Vaksin Sinovac Menggunakan Algoritma Support Vector Machine (Svm) Dan K-Nearest Neighbor (Knn),” Inf. Syst. J., vol. 4, no. 2, pp. 42–42, 2021, doi: https://doi.org/10.24076/infosjournal.2021v4i2.687.
T. D. Putra and D. Oktafiani, “Klasifikasi Sentimen Postingan Sosial Media Menggunakan Machine Learning Random Forest dan Naïve Bayes,” Innov. J. Soc. Sci. Res., vol. 5, no. 1, pp. 2338–2347, 2025, doi: https://doi.org/10.31004/innovative.v5i1.17935.
J. W. Iskandar and Y. Nataliani, “Perbandingan Naïve Bayes, SVM, dan k-NN untuk Analisis Sentimen Gadget Berbasis Aspek,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 6, pp. 1120–1126, 2021, doi: 10.29207/resti.v5i6.3588.
P. Arsi and R. Waluyo, “Analisis Sentimen Wacana Pemindahan Ibu Kota Indonesia Menggunakan Algoritma Support Vector Machine (SVM),” J. Teknol. Inf. dan Ilmu Komput., vol. 8, no. 1, p. 147, 2021, doi: 10.25126/jtiik.0813944.
M. Rafli Ghufron et al., :“//creativecommons.org/licenses/by-sa/4.0/) 307 J-INTECH (Journal of Information and Technology) Analisis Sentimen Pengguna Twitter Terhadap Pemilu 2024 Berbasis Model XLM-T,” Inf. adn Technol., vol. 11 no 2, no. 204, pp. 307–315, 2023, doi: https://doi.org/10.32664/j-intech.v11i2.1013.
A. Miftahusalam, A. F. Nuraini, A. A. Khoirunisa, and H. Pratiwi, “Comparison of Random Forest, Naïve Bayes, and Support Vector Machine Algorithms in Analyzing Twitter Sentiment Regarding Public Opinion on the Removal of Honorary Employees,” Semin. Nas. Off. Stat., vol. 2022, no. 1, pp. 563–572, 2022.
N. Fitriyah, B. Warsito, and D. A. I. Maruddani, “Analisis Sentimen Gojek Pada Media Sosial Twitter Dengan Klasifikasi Support Vector Machine (Svm,” J. Gaussian, vol. 9, no. 3, pp. 376–390, 2020, doi: 10.14710/j.gauss.v9i3.28932.
T. Gori, A. Sunyoto, and H. Al Fatta, “Preprocessing Data dan Klasifikasi untuk Prediksi Kinerja Akademik Siswa,” J. Teknol. Inf. dan Ilmu Komput., vol. 11, no. 1, pp. 215–224, 2024, doi: 10.25126/jtiik.20241118074.
L. A. Andika, P. A. N. Azizah, and R. Respatiwulan, “Analisis Sentimen Masyarakat terhadap Hasil Quick Count Pemilihan Presiden Indonesia 2019 pada Media Sosial Twitter Menggunakan Metode Naive Bayes Classifier,” Indones. J. Appl. Stat., vol. 2, no. 1, p. 34, 2019, doi: 10.13057/ijas.v2i1.29998.
H. Syah and A. Witanti, “Analisis Sentimen Masyarakat Terhadap Vaksinasi Covid-19 Pada Media Sosial Twitter Menggunakan Algoritma Support Vector Machine (Svm),” J. Sist. Inf. dan Inform., vol. 5, no. 1, pp. 59–67, 2022, doi: 10.47080/simika.v5i1.1411.
W. G. S. Parwita, “Pengujian Akurasi Sistem Rekomendasi Berbasis Content-Based Filtering,” Inform. Mulawarman J. Ilm. Ilmu Komput., vol. 14, no. 1, p. 27, 2019, doi: 10.30872/jim.v14i1.1272.
M. Syarifuddinn, “Analisis Sentimen Opini Publik Terhadap Efek Psbb Pada Twitter Dengan Algoritma Decision Tree,Knn, Dan Naïve Bayes,” INTI Nusa Mandiri, vol. 15, no. 1, pp. 87–94, 2020, doi: 10.33480/inti.v15i1.1433.
P. I. Purnama and R. R. Suryono, “Analisis Sentimen Acara Clash of Champions dengan Algoritma Naïve Bayes dan Support Vector Machine,” Build. Informatics, Technol. Sci., vol. 6, no. 4, pp. 2277–2287, 2025, doi: 10.47065/bits.v6i4.6575.
L. Apriani and N. Hendrastuty, “Analisis Sentiment Terhadap Diabetes Menggunakan Algoritma Naïve Bayes , Random Forest , SVM Pada Media Sosial X,” Build. Informatics, Technol. Sci., vol. 6, no. 4, pp. 2469–2479, 2025, doi: 10.47065/bits.v6i4.6941.
D. Darwis, N. Siskawati, and Z. Abidin, “Penerapan Algoritma Naive Bayes Untuk Analisis Sentimen Review Data Twitter Bmkg Nasional,” J. Tekno Kompak, vol. 15, no. 1, p. 131, 2021, doi: 10.33365/jtk.v15i1.744.
M. R. A. Nasution and M. Hayaty, “Perbandingan Akurasi dan Waktu Proses Algoritma K-NN dan SVM dalam Analisis Sentimen Twitter,” J. Inform., vol. 6, no. 2, pp. 226–235, 2019, doi: 10.31311/ji.v6i2.5129.
M. R. Adrian, M. P. Putra, M. H. Rafialdy, and N. A. Rakhmawati, “Perbandingan Metode Klasifikasi Random Forest dan SVM Pada Analisis Sentimen PSBB,” J. Inform. Upgris, vol. 7, no. 1, pp. 36–40, 2021, doi: 10.26877/jiu.v7i1.7099.
P. G. Dewata, A. Rizky, and H. Irsyad, “Analisis Sentimen Terhadap Boikot Produk Israel Menggunakan Algoritma Naive Bayes Dan SMOTE,” J. Rein (Rekayasa Inform., vol. 1, no. 1, pp. 7–15, 2024, [Online]. Available: https://rekayasainformatika.com/index.php/JREIN/article/view/1
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