Analyzing Indonesian Football Sentiment Towards PSSI Performance Using Support Vector Machines

Faturrahman Hakim, Yuli Astuti

Abstract


Football is a popular and widely engaged sport in Indonesia, attracting individuals across various age groups, including teenagers, adults, and children. The Indonesian Football Association (PSSI), established on April 19, 1930, originally named the All-Indonesian Football Association, is the governing body responsible for managing and overseeing football activities in the country. Despite its long history, PSSI has faced significant criticism for its perceived lack of professionalism in handling and managing Indonesian football. This discontent was notably amplified in the wake of the cancellation of the U-20 World Cup, leading to a surge of negative sentiments on social media platforms, particularly Twitter. This study aims to analyze public opinion regarding PSSI's performance. Public opinion, which emerges in response to various events, tends to be diverse due to the differing perspectives of individuals. The research focuses on assessing the balance between positive and negative sentiments towards PSSI's performance. By employing a comprehensive approach to sentiment analysis, including stages such as data preprocessing, labeling, modeling, and evaluation, this study provides a detailed examination of public sentiment. The methodology involves the application of the Support Vector Machine (SVM) algorithm across four tests with different data splits and the use of the SMOTE technique to address class imbalance. The findings reveal that the fourth test yielded the most effective results in sentiment classification, achieving an accuracy of 70.75\%, precision of 67.16\%, recall of 68.18\%, and an F1 score of 67.66\%


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References


REFERENCES

SUCI RAHAYUU, “Shin Tae-yong Sakit Hati Piala Dunia U20 Batal Digelar di Indonesia,” https://bola.kompas.com/read/2023/03/30/22000068/shin-tae-yong-sakit-hati-piala-dunia-u20-batal-digelar-di-indonesia, Mar. 30, 2023

A. Witanti, B. Yogyakarta Jl Raya Wates-Jogjakarta, K. Sedayu, K. Bantul, and D. Istimewa Yogyakartalamat, “ANALISIS SENTIMEN MASYARAKAT TERHADAP VAKSINASI COVID-19 PADA MEDIA SOSIAL TWITTER MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE (SVM),” Jurnal Sistem Informasi dan Informatika (Simika) P-ISSN, vol. 5, pp. 2622–6901, 2022.

A. S. Wicaksono, “ANALISIS SENTIMEN SEPAKBOLA INDONESIA MENGGUNAKAN SUPPORT VECTOR MACHINE,” 2019.

M. F. Asshiddiqi and K. M. Lhaksmana, “Perbandingan Metode Decision Tree dan Support Vector Machine untuk Analisis Sentimen pada Instagram Mengenai Kinerja PSSI.”

J. M. M. S. Reino Prajamukti, “KLASIFIKASI DAN ANALISIS SENTIMEN PADA DATA,” 2021.

O. I. Gifari, M. Adha, I. Rifky Hendrawan, F. Freddy, and S. Durrand, “Analisis Sentimen Review Film Menggunakan TF-IDF dan Support Vector Machine,” JIFOTECH (JOURNAL OF INFORMATION TECHNOLOGY, vol. 2, no. 1, 2022.

R. Tineges, A. Triayudi, and I. D. Sholihati, “Analisis Sentimen Terhadap Layanan Indihome Berdasarkan Twitter Dengan Metode Klasifikasi Support Vector Machine (SVM),” JURNAL MEDIA INFORMATIKA BUDIDARMA, vol. 4, no. 3, p. 650, Jul. 2020, doi: 10.30865/mib.v4i3.2181.

C. Zai and T. Komputer, “IMPLEMENTASI DATA MINING SEBAGAI PENGOLAHAN DATA.”

D. Siregar, F. Ladayya, N. Z. Albaqi, and B. M. Wardana, “Penerapan Metode Support Vector Machines (SVM) dan Metode Naïve Bayes Classifier (NBC) dalam Analisis Sentimen Publik terhadap Konsep Child-free di Media Sosial Twitter,” Jurnal Statistika dan Aplikasinya, vol. 7, no. 1, 2023.

N. Hendrastuty, A. Rahman Isnain, and A. Yanti Rahmadhani, “Analisis Sentimen Masyarakat Terhadap Program Kartu Prakerja Pada Twitter Dengan Metode Support Vector Machine,” vol. 6, no. 3, 2021, [Online]. Available: http://situs.com

M. G. A. P. M. F. A. Aji Prasetya Wibawa, “Metode-metode Klasifikasi,” Pros. Semin. Ilmu Komput. dan Teknol. Inf., 1st ed., vol. 3. 2018.

S. Raja Manaek Pakpahan, “Analisis Sentimen Tentang Opini Performa Klub Sepak Bola Pada Dokumen Twitter Menggunakan Support Vector Machine Dengan Perbaikan Kata Tidak Baku,” 2019. [Online]. Available: http://j-ptiik.ub.ac.id

R. N. Melinda, L. Meitya Ningrum, I. B. Suryabrata, G. Swarna Bayu, A. Dwipa, and T. P. Sukoco, “Program Perhitungan RAB Pekerjaan Struktur Baja (WF BEAM) Menggunakan Bahasa Python,” TIERS Information Technology Journal, vol. 2, no. 1, pp. 31–38, 2021, [Online]. Available: https://journal.undiknas.ac.id/index.php/tiers

J. Khatib Sulaiman, P. Antibiotik di Indonesia Herdianti Darwis, N. Wanaspati, S. Anraeni, and I. Artikel Abstrak, “Support Vector Machine untuk Analisis Sentimen Masyarakat Terhadap Penggunaan Antibiotik di Indonesia,” Indonesian Journal of Computer Science Attribution, vol. 12, no. 4, p. 2196.

R. Rahman Salam, M. Fajri Jamil, and Y. Ibrahim, “Analisis Sentimen Terhadap Bantuan Langsung Tunai (BLT) Bahan Bakar Minyak (BBM) Menggunakan Support Vector Machine,” vol. 3, pp. 27–35, 2023.




DOI: http://dx.doi.org/10.26798/jiss.v3i1.1330

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JOURNAL OF INTELLIGENT SOFTWARE SYSTEMS

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