Sentiment Analysis of Public Opinion on PSSI Naturalization Program Based on Social Media Using the Naive Bayes Algorithm
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Abstract
Football is the most popular sport in Indonesia and receives tremendous support from the public. However, the player naturalization program initiated by PSSI (the Indonesian Football Association) has become an issue that has captured public attention, generating diverse opinions on social media, particularly on the Twitter platform. This study aims to analyze public sentiment toward the naturalization program by applying the Naïve Bayes classification method. The data used consists of tweets containing keywords related to PSSI naturalization, naturalized players, descendant players, national team naturalization, and overseas players for the national team. The analysis process includes several stages of data preprocessing—such as text cleaning, normalization, and stop word removal—feature extraction using TF-IDF, and sentiment classification using the Naïve Bayes algorithm to categorize opinions into positive, negative, and neutral sentiments. The Naïve Bayes model achieved an accuracy of 0.65, precision of 0.42, recall of 0.65, and an F1-score of 0.51. It performed well in classifying neutral tweets but was less effective in identifying positive and negative sentiments. Overall, the Naïve Bayes method can be utilized for sentiment analysis; however, its classification performance is not yet optimal due to the limited amount of data.
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References
G. Sumantri and B. S. H. Marwoto, “Analisis Sentimen Di Twitter Terkait Tim Nasional Sepak Bola Indonesia Menggunakan Metode Support Vector Machine,” Jurnal Kajian dan Terapan Matematika, vol. 10, no. 2, pp. 96–104, 2024, doi: 10.21831/JKTM.V10I2.19561.
A. B. Ferdiyawan, Nurasiah, I. F. Muldani, W. Winanti, and S. Basuki, “Analisis Sentimen Pada Media Sosial Twitter(X) Terhadap Pemain Diaspora Di Tim Nasional Sepak Bola Indonesia Studi Kasus Pesepakbola Mees Hilgers Dan Eliano Reinjders Menggunakan Metode Naïve Bayes Dan Support Vector Machine (SVM),” ipsikom, vol. 13, no. 1, pp. 63–70, 2025, doi: 10.58217/IPSIKOM.V13I1.50.
T. Juniardi and C. A. Sugianto, “Analisis Sentimen Tim Nasional Sepak Bola Indonesia Di Turnamen Piala Dunia U-17 Indonesia Pada Twitter (X) Menggunakan Algortima Naïve Bayes,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 12, no. 3S1, pp. 2830–7062, 2024, doi: 10.23960/JITET.V12I3S1.5188.
W. Alfiyani, D. A. Fatah, and F. Irhamni, “Penerapan Algoritma Naïve Bayes Untuk Analisis Sentimen Pada Media Sosial X Terhadap Performa Tim Nasional Sepak Bola Indonesia Di Era Kepemimpinan Shin Tae-Yong,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 9, no. 3, pp. 3969–3977, 2025, doi: 10.36040/JATI.V9I3.13451.
R. Rikky, M. Graciela, and H. Irsyad, “Klasifikasi Opini Masyarakat Terhadap Naturalisasi Pemain Sepak Bola Menggunakan KNN dan SMOTE,” Applied Information Technology and Computer Science (AICOMS), vol. 3, no. 1, pp. 21–27, 2024, doi: 10.58466/AICOMS.V3I1.1547.
F. M. Sarimole and K. Kudrat, “Analisis Sentimen Terhadap Aplikasi Satu Sehat Pada Twitter Menggunakan Algoritma Naive Bayes Dan Support Vector Machine,” Jurnal Sains dan Teknologi, vol. 5, no. 3, pp. 783–790, 2024, doi: 10.55338/SAINTEK.V5I3.2702.
N. Raisa and N. Riza, “Sentimen Analisis Terhadap Opini Masyarakat Mengenai Drama Korea Pada Twitter Menggunakan Metode Naïve Bayes,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 7, no. 2, pp. 1312–1320, 2023, doi: 10.36040/JATI.V7I2.6765.
S. Lutfiani, R. Astuti, and F. M. Basysyar, “Analisis Sentimen Pengaruh Media Sosial Terhadap Minat Beli Skincare Pada Remaja Di Indonesia Menggunakan Algoritma Naïve Bayes,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 8, no. 3, pp. 2957–2961, 2024, doi: 10.36040/JATI.V8I3.9614.
N. Musatari, W. Suardi, and U. Syukri, “Analisis Sentimen Media Sosial: Penerapan E-katalog Dalam Pengadaan Barang Dan Jasa Di Indonesia,” PRAJA: Jurnal Ilmiah Pemerintahan, vol. 10, no. 3, pp. 193–200, 2022, doi: 10.55678/PRJ.V10I3.702.
M. G. R. Lubis, D. S. Sitompul, T. M. Giovanni, F. Ramadhani, and S. Dewi, “Evaluasi Kinerja Algoritma Support Vector Machine (SVM) Dalam Analisis Sentimen Publik Terhadap Naturalisasi Timnas Indonesia di Twitter,” Journal of Accounting Law Communication and Technology, vol. 2, no. 1, pp. 81–89, 2025, doi: 10.57235/JALAKOTEK.V2I1.4180.
M. E. Bere, Y. P. K. Kelen, H. H. Ullu, and B. Baso, “Implementasi Algoritma Naive Bayes Classifier Terhadap Analisis Sentimen Kondisi Stunting di Indonesia Pada Media Sosial X,” Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI), vol. 7, no. 4, pp. 598–605, 2024, doi: 10.32672/jnkti.v7i4.7752.
M. T. Usniaty, Y. Setyawan, and R. D. Bekti, “Analisis Sentimen Tentang Implementasi Hak Asasi Manusia Di Indonesia Pada Media Sosial Twitter Menggunakan Naïve Bayes Dan Support Vector Machine,” Jurnal Statistika Industri dan Komputasi, vol. 9, no. 1, pp. 75–84, 2024, doi: 10.34151/STATISTIKA.V9I1.4846.
N. Tonellotto, “Lecture Notes on Neural Information Retrieval,” arXiv, 2022, Accessed: Dec. 13, 2025. [Online]. Available: https://arxiv.org/abs/2207.13443v2.
S. M. Abdullah, R. A. Sulaiman, and M. M. Ali, “Text preprocessing techniques for sentiment analysis: A review,” Journal of Theoretical and Applied Information Technology, vol. 98, no. 14, pp. 2848–2860, 2020.
A. Krouska, C. Troussas, and M. Virvou, “The effect of preprocessing techniques on Twitter sentiment analysis,” IISA 2016 - 7th International Conference on Information, Intelligence, Systems and Applications, 2016, doi: 10.1109/IISA.2016.7785373.
J. Jefriyanto, N. Ainun, M. Arif, and A. Ardha, “Application of Naïve Bayes Classification to Analyze Performance Using Stopwords,” Journal of Information System, Technology and Engineering, vol. 1, no. 2, pp. 49–53, 2023, doi: 10.61487/JISTE.V1I2.15.
R. Alsini, “Analysis of Real Time Twitter Sentiments using Deep Learning Models,” Journal of Applied Data Sciences, vol. 4, no. 4, pp. 480–489, 2023, doi: 10.47738/JADS.V4I4.146.
M. Garg, S. Kumar, and A. K. J. Saudagar, Natural language processing and information retrieval : principles and applications. CRC Press, 2025.
T. Mandl and J. M. Struß, Information retrieval: core techniques, issues, current and future developments. Edward Elgar Publishing, 2024.
S. Styawati, A. R. Isnain, N. Hendrastuty, and L. Andraini, “Comparison of Support Vector Machine and Naïve Bayes on Twitter Data Sentiment Analysis,” Jurnal Informatika: Jurnal Pengembangan IT, vol. 6, no. 1, pp. 56–60, 2021, doi: 10.30591/JPIT.V6I1.3245.
A. E. Nugroho and F. D. Rahman, “Preprocessing optimization for sentiment analysis using Indonesian Twitter dataset,” Journal of Data Science and Its Applications, vol. 3, no. 1, pp. 45–56, 2023, doi: 10.52502/jdsa.v3i1.150.
L. Liu, Y. Yu, and M. Sun, “A comparison of text classification models for sentiment analysis on social media,” IEEE Access, vol. 9, pp. 146215–146229, 2021, doi: 10.1109/ACCESS.2021.3119052.
D. W. Putra and M. A. Hadi, “Optimization of text classification using TF-IDF and Naive Bayes for Indonesian language,” Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK), vol. 10, no. 2, pp. 287–295, 2023, doi: 10.25126/jtiik.202310287.
M. D. H. Jasy, S. Al Hasan, M. I. K. Sagor, A. Noman, and J. M. Ji, “A Performance Evaluation of Sentiment Classification Applying SVM, KNN, and Naive Bayes,” Proceedings - 2021 International Conference on Computing, Networking, Telecommunications and Engineering Sciences Applications, CoNTESA 2021, pp. 56–60, 2021, doi: 10.1109/CONTESA52813.2021.9657115.