Analisis Sentimen Terhadap Pemain Naturalisasi dan Lokal Tim Nasional Sepakbola Indonesia Menggunakan Support Vector Machine
-
Fadlan Arrazak
*
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru,
Indonesia
- M Afdal Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
- Rice Novita Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
- Megawati Megawati Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia (*) Corresponding Author
Abstract
The inclusion of naturalized players in Indonesia's national football team has sparked diverse public reactions, particularly on social media platforms like Twitter. This study aims to compare public opinion toward naturalized and local players through sentiment analysis. A total of 2,342 tweets were categorized into three sentiment classes: positive, neutral, and negative. Naturalized players received a higher number of positive sentiments, totaling 809, compared to 333 negative and 231 neutral sentiments. In contrast, local players gained 465 positive sentiments, 317 negative, and 187 neutral, indicating a generally more favorable perception of naturalized players among the public. Further analysis was conducted using the Support Vector Machine (SVM) classification algorithm along with the SMOTE technique for data balancing, focusing on five key aspects: performance, experience, physical condition, adaptability, and communication. The classification results showed that naturalized players outperformed in physical condition with an accuracy of 96 percent, followed by performance and adaptability, each at 90 percent. On the other hand, local players showed superiority only in communication with an accuracy of 92 percent. In terms of precision and recall, naturalized players again led in physical condition, achieving 97 percent precision and 96 percent recall, while local players excelled in communication with both precision and recall at 92 percent. These findings offer valuable insights for policymakers and football organizations in formulating more effective naturalization strategies.
Downloads
References
M. Graciela and dan Hafiz Irsyad, “Klasifikasi Opini Masyarakat Terhadap Naturalisasi Pemain Sepak Bola Menggunakan KNN dan SMOTE,” Applied Information Technology and Computer Science (AICOMS), Vol 3, No 1, Juni 2024, doi: https://doi.org/10.58466/aicoms.v3i1.1547.
N. M. Sania, N. Baitillah, M. H. Indriani, F. Fernanda, and T. Aditya, “Survei Kepuasan Opini Publik terhadap Kebijakan Naturalisasi Pemain PSSI: dalam Upaya Meningkatkan Prestasi Timnas Indonesia,” Indonesian Journal of Public Administration Review, vol. 2, no. 2, p. 19, Jan. 2025, doi: 10.47134/par.v2i2.3564.
D. Rizky, P. Jaya, and S. Lestari, “Analisis Sentimen Naturalisasi Tim Nasional Indonesia U-23 di Era Shin Tae-yong Menggunakan Algoritma Naïve Bayes dan K-Nearest Neighbors,” Jurnal Indonesia : Manajemen Informatika dan Komunikasi (JIMIK), Vol 5, No 3, 2024, doi: https://doi.org/10.35870/jimik.v5i3.1024.
M. P. Silitonga and D. Kristina, “Naturalisasi Pemain Asing Dalam Hukum Konstitusi Indonesia,” Honeste Vivere, vol. 35, no. 1, pp. 129–137, Jan. 2025, doi: 10.55809/hv.v35i1.429.
H. Sinaga, T. Br Sembiring, and I. Artikel, “Perlakuan Penghinaan Pemain Naturalisasi Maupun Lokal Sepak Bola Indonesia Di Media Sosial Dan Hukum Yang Berlaku,” Journal of International Multidisciplinary Research, vol. 2, no. 1, 2024
S. Ramdani and T. Bustomi, “Analisis Sentimen Terhadap Pemain Diaspora Timnas Indonesia Pada Media Sosial Instagram Menggunakan Metode Support Vector Machine (SVM),” Sebatik, 2024, doi: 10.46984/sebatik.v28i2.0000.
R. Asrianto and M. Herwinanda, “Analisis Sentimen Kenaikan Harga Kebutuhan Pokok Dimedia Sosial Youtube Menggunakan Algoritma Support Vector Machine,” Jurnal CoSciTech (Computer Science and Information Technology), vol. 3, no. 3, pp. 431–440, Dec. 2022, doi: 10.37859/coscitech.v3i3.4368.
N. Hendrastuty, A. Rahman Isnain, and A. Yanti Rahmadhani, “Analisis Sentimen Masyarakat Terhadap Program Kartu Prakerja Pada Twitter Dengan Metode Support Vector Machine,” Jurnal Informatika: Jurnal pengembangan IT (JPIT), vol. 6, no. 3, 2021, doi: https://doi.org/10.30591/jpit.v6i3.2870.
M. Cakal Cendekia, G. Yulisatria, Y. H. Bhakti, M. Suhariyanti, and M. F. Dongoran, “Strategi Pelatihan yang Efektif untuk Pengembangan Pemain Muda Sepakbola: Sebuah Tinjauan Literatur,” Jurnal Ilmiah STOK Bina Guna Medan (JISBG), vol. 13, no 1, 2025, doi: https://doi.org/10.55081/jsbg.v13i1.3668.
D. Musfiroh et al., “Sentiment Analysis of Online Lectures in Indonesia from Twitter Dataset Using InSet Lexicon Analisis Sentimen terhadap Perkuliahan Daring di Indonesia dari Twitter Dataset Menggunakan InSet Lexicon,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 1, no. 1, pp. 24–33, 2021, doi: https://doi.org/10.57152/malcom.v1i1.20.
R. T. Aldisa and P. Maulana, “Analisis Sentimen Opini Masyarakat Terhadap Vaksinasi Booster COVID-19 Dengan Perbandingan Metode Naive Bayes, Decision Tree dan SVM,” Building of Informatics, Technology and Science (BITS), vol. 4, no. 1, pp. 106–109, Jun. 2022, doi: 10.47065/bits.v4i1.1581.
E. A. Lisangan et al., “Implementasi Naive Bayes pada Analisis Sentimen Opini Masyarakat di Twitter Terhadap Kondisi New Normal di Indonesia,” KONSTELASI: Konvergensi Teknologi dan Sistem Informasi, vol 2, no 1, 2022, doi: https://doi.org/10.24002/konstelasi.v2i1.5609.
A. Rachmadana Ismail, R. Bagus, and F. Hakim, “Implementasi Lexicon Based Untuk Analisis Sentimen Dalam Mengetahui Trend Wisata Pantai Di DI Yogyakarta Berdasarkan Data Twitter,” Emerging Statistics and Data Science Journal (ESDS), vol 11, no 1, 2023, doi: https://doi.org/10.20885/esds.vol1.iss.1.art5.
D. Farah Zhafira, and B. Rahayudi, “Analisis Sentimen Kebijakan Kampus Merdeka Menggunakan Naive Bayes dan Pembobotan TF-IDF Berdasarkan Komentar pada YouTube,” Jurnal Sistem Informasi, Teknologi Informasi, Dan Edukasi Sistem Informasi, vol 2, no 1, 2021, doi: https://doi.org/10.25126/justsi.v2i1.24.
O. : Rifqy, M. Turjaman, and I. Budi, “Analisis Sentimen Berbasis Aspek Marketing Mix Terhadap Ulasan Aplikasi Dompet Digital (Studi Kasus: Aplikasi LinkAja pada Twitter),” Jurnal Darma Agung, Vol 30, No 2, 2022, doi: http://dx.doi.org/10.46930/ojsuda.v30i2.1672.
R. A. Husen, R. Astuti, L. Marlia, R. Rahmaddeni, and L. Efrizoni, “Analisis Sentimen Opini Publik pada Twitter Terhadap Bank BSI Menggunakan Algoritma Machine Learning,” MALCOM: Indonesian Journal of Machine Learning and Computer Science, vol. 3, no. 2, pp. 211–218, Oct. 2023, doi: 10.57152/malcom.v3i2.901.
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, 2025, doi: 10.29408/edumatic.v9i1.28987.
D. Atika, A. Ari Aldino, “Term Frequency-Inverse Document Frequency Support Vector Machine untuk Analisis Sentimen Opini Masyarakat terhadap Tekanan Mental pada Media Sosial Twitter,” JURNAL Teknologi dan Sistem Informasi (JTSI), 2022. doi: https://doi.org/10.33365/jtsi.v3i4.2054.
P. M. Susanti, M. Afdal, I. Permana, and A. Marsal, “Klasifikasi Sentimen Pengguna X Terhadap Pemboikotan Produk Pro Israel Menggunakan Algoritma Machine Learning,” Technology and Science (BITS), vol. 6, no. 4, 2025, doi: 10.47065/bits.v6i4.6533.
V. Fitriyana et al., “Analisis Sentimen Ulasan Aplikasi Jamsostek Mobile Menggunakan Metode Support Vector Machine,” Jurnal Buana Informatika Jurnal Buana Informatika, Vol 14, No 1, 2023. doi: https://doi.org/10.24002/jbi.v14i01.6909.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Analisis Sentimen Terhadap Pemain Naturalisasi dan Lokal Tim Nasional Sepakbola Indonesia Menggunakan Support Vector Machine
Pages: 939-948
Copyright (c) 2025 Fadlan Arrazak, M Afdal, Rice Novita, Megawati Megawati
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
