Published: 2025-01-01
Analisis Sentimen Kepuasan Publik Terhadap Masa Kepemimpinan Shin Tae Yong Menggunakan Algoritma Naïve Bayes
DOI: 10.35870/jtik.v9i1.3020
Downloads
Abstract
Shin Tae Yong is the coach of the Indonesian national team who has been a football player in South Korea and has coached the South Korean national team at the 2018 World Cup in Russia. Many people watch or pay attention to Shin Tae Yong's behavior and behavior when coaching the Indonesian national team. Shin Tae Yong has considerable worry with the Indonesian national team because of his strategy. However, there are several media that frame Shin Tae Yong's news differently so that differences in viewpoints and opinions on Shin Tae Yong are controversial, inviting many people to give their opinions. Therefore, people choose social media as a place to channel opinions. In this study, we will take tweets from X with search keywords for Shin Tae Yong and the Indonesian national team to process and classify the text using the sentiment analysis method. The text classification process is divided into two classes, namely positive sentiment classes and negative sentiment classes. The data used amounted to 2495 data that had been cleansed, which amounted to 2.348 Positive sentiment data and 147 data with negative sentiments so that they can be presented 98.94% positive and 60.00% negative, based on the classification of the Naïve Bayes algorithm model, using a split comparative data 0.8 : 0.2 With the value of k=3 for Shin Tae Yong's dataset, an accuracy value of 96.67%.
Keywords
Data Mining ; Shin Tae Yong ; Naïve Bayes Algorithm ; Football
Article Information
This article has been peer-reviewed and published in the Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
-
Issue: Vol. 9 No. 1 (2025)
-
Section: Computer & Communication Science
-
Published: %750 %e, %2025
-
License: CC BY 4.0
-
Copyright: © 2024 Authors
-
DOI: 10.35870/jtik.v9i1.3020
AI Research Hub
This article is indexed and available through various AI-powered research tools and citation platforms. Our AI Research Hub ensures that scholarly work is discoverable, accessible, and easily integrated into the global research ecosystem. By leveraging artificial intelligence for indexing, recommendation, and citation analysis, we enhance the visibility and impact of published research.
Pramudya Nugraha
Program Studi Sistem Informasi, Fakultas Teknologi Informatika, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia.
Rasiban
Program Studi Sistem Informasi, Fakultas Teknologi Informatika, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia.
Frencis Matheos Sarimole
Program Studi Sistem Informasi, Fakultas Teknologi Informatika, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia.
Tundo
Program Studi Sistem Informasi, Fakultas Teknologi Informatika, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia.
-
Aponno, J. C. (2022). Penerapan Algoritma Sentimen Analysis dan Naïve Bayes terhadap opini pengunjung di tempat wisata pantai Pintu Kota, Kota Ambon. JATISI (Jurnal Teknik Informatika dan Sistem Informasi), 9(4), 3180-3188. DOI: https://doi.org/10.35957/jatisi.v9i4.2697.
-
Apriani, R., & Gustian, D. (2019). Analisis Sentimen dengan Naïve Bayes Terhadap Komentar Aplikasi Tokopedia. Jurnal Rekayasa Teknologi Nusa Putra, 6(1), 54-62.
-
Bara, E. B., Nasution, K. A., Ginting, R. Z., & Kartini, K. (2022). Penelitian tentang Twitter. Jurnal Edukasi Nonformal, 3(2), 167-172.
-
Damayanti, A., & Maulidiyah, A. K. (2023). Analisis Sentimen Masyarakat Terhadap Pembatalan Piala Dunia U20 di Indonesia Pada Media Sosial Twitter Dengan Klasifikasi Support Vector Machine (SVM). Jurnal Ilmiah Terapan, Sains dan Teknologi (JITSI), 1(2), 97-103. DOI: https://doi.org/10.25139/jitsi.v1i2.6607.
-
Hidayat, A. M., & Syafrullah, M. (2018). Algoritma Naïve Bayes Dalam Analisis Sentimen Untuk Klasifikasi Pada Layanan Internet PT. XYZ. Telematika MKOM, 9(2), 91-95.
-
Lisangan, E. A., Gormantara, A., & Carolus, R. Y. (2022). Implementasi Naive Bayes pada Analisis Sentimen Opini Masyarakat di Twitter Terhadap Kondisi New Normal di Indonesia. KONSTELASI: Konvergensi Teknologi dan Sistem Informasi, 2(1). DOI: https://doi.org/10.24002/konstelasi.v2i1.560.
-
Mahbubah, L. D., & Zuliarso, E. (2019). Analisa Sentimen Twitter Pada Pilpres 2019 Menggunakan Algoritma Naive Bayes.
-
Munawaroh, A., Ridhoi, R., & Rudiman, R. (2024). SENTIMENT ANALYSIS DENGAN NAÏVE BAYES BERBASIS ORANGE TERHADAP RESIKO PEMBANGUNAN IKN. JATI (Jurnal Mahasiswa Teknik Informatika), 8(1), 587-592. DOI: https://doi.org/10.36040/jati.v8i1.8454.
-
Nadhifa, F. H. (2023). ANALISIS FRAMING TERHADAP PEMBERITAAN PELATIH TIMNAS INDONESIA. CommLine, 7(2), 106-111. DOI: http://dx.doi.org/10.36722/cl.v7i2.1313.
-
Prajamukti, R., Jayanta, J., & Santoni, M. M. (2021). Klasifikasi Dan Analisis Sentimen Pada Data Twitter Menggunakan Algoritma Naïve Bayes (Studi Kasus: Timnas Indonesia Senior, U-23, Dan U-19). PROSIDING SEINASI-KESI, 4(1), 102-109.
-
Pratama, A. E., Ariesta, A., & Gata, G. (2022). Analisis Sentimen Masyarakat terhadap Tim Nasional Indonesia pada Piala AFF 2020 Menggunakan Algoritma K-Nearest Neighbors. Jurnal TICOM: Technology of Information and Communication, 10(3), 187-196. OI: https://doi.org/10.70309/ticom.v10i3.33.
-
Purbayanto, B., & Suharsono, T. N. (2023). Analisis Sentimen Pengguna X terhadap Chatgpt dengan Algoritme Naive Bayes. Jurnal Telematika, 18(2), 63-71. DOI: https://doi.org/10.61769/telematika.v18i2.614.
-
Putra, A. P., & Syafira, A. F. (2023). Analisis Sentimen Data Twitter Topik Politik Dengan Metode Naive Bayes Dan Convolutional Neural Networks (Cnn). Jurnal Ilmiah Wahana Pendidikan, 9(20), 36-41. DOI: https://doi.org/10.5281/zenodo.8396579.
-
Ratnaningtyas, R. P., & Muhammad, Y. A. (2023). Analisis Pemberitaan Timnas Indonesia pada Media Daring. MUKASI: Jurnal Ilmu Komunikasi, 2(1), 45-52. DOI: https://doi.org/10.54259/mukasi.v2i1.1492.
-
Sidik, M. H., Widiyanesti, S., & Ramadhani, D. P. (2022). Analisis Sentimen dan Topic Modelling Terhadap Tim Nasional Indonesia di Kejuaraan AFF Suzuki Cup 2020 Berdasarkan Opini Pengguna Twitter. eProceedings of Management, 9(5).
-
Susana, H. (2022). Penerapan Model Klasifikasi Metode Naive Bayes Terhadap Penggunaan Akses Internet. Jurnal Riset Sistem Informasi Dan Teknologi Informasi (JURSISTEKNI), 4(1), 1-8.
-
Susanto, A., & Dzulkarnain, I. A. (2023). Analisis Sentimen Data Twitter Topik Ekonomi Dan Industri Dengan Metode Naive Bayes Dan Random Forest. Jurnal Ilmiah Wahana Pendidikan, 9(20), 59-65. DOI: https://doi.org/10.5281/zenodo.8398895.
-
Widowati, T. T., & Sadikin, M. (2020). Analisis Sentimen Twitter terhadap Tokoh Publik dengan Algoritma Naive Bayes dan Support Vector Machine. Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer, 11(2), 626-636. DOI: https://doi.org/10.24176/simet.v11i2.4568.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Copyright Retention and Open Access License
Authors retain copyright of their work and grant the journal non-exclusive right of first publication under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license allows unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
2. Rights Granted Under CC BY 4.0
Under this license, readers are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, including commercial use
- No additional restrictions — the licensor cannot revoke these freedoms as long as license terms are followed
3. Attribution Requirements
All uses must include:
- Proper citation of the original work
- Link to the Creative Commons license
- Indication if changes were made to the original work
- No suggestion that the licensor endorses the user or their use
4. Additional Distribution Rights
Authors may:
- Deposit the published version in institutional repositories
- Share through academic social networks
- Include in books, monographs, or other publications
- Post on personal or institutional websites
Requirement: All additional distributions must maintain the CC BY 4.0 license and proper attribution.
5. Self-Archiving and Pre-Print Sharing
Authors are encouraged to:
- Share pre-prints and post-prints online
- Deposit in subject-specific repositories (e.g., arXiv, bioRxiv)
- Engage in scholarly communication throughout the publication process
6. Open Access Commitment
This journal provides immediate open access to all content, supporting the global exchange of knowledge without financial, legal, or technical barriers.