Twitter Sentiment Analysis Classification to Assess Public Opinion on Football Matches Using the Naïve Bayes Method

Yuli Astuti, Hafiidh Khoiru Pradana, Dewi Anisa Istiqomah, supriatin supriatin, Ninik Tri Hartati

Abstract


 The Kanjuruhan tragedy has attracted many comments on various social media platforms. This research will compare the number of positive and negative comments on Twitter and social media and determine the accuracy of the classification method used. The data used in this study consisted of 2052 pieces, consisting of 1015 positive and 1037 negative pieces. To determine the effect of the amount of training data on the resulting accuracy, testing will be carried out three times with different combinations of training data and test data, namely 70:30, 80:20, and 90:10. The results of this study obtained the highest accuracy value of 79.6%. This program can be developed for other social media platforms such as Facebook, Instagram, and others

Full Text:

PDF

References


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.

M. Astiningrum, M. Hani, Y. Rahmat, and Y. Pradana, “TERHADAP PERFORMA TIMNAS SEPAK,” 2020.

dan S. Prajamukti, Jayanta, “KLASIFIKASI DAN ANALISIS SENTIMEN PADA DATA TWITTER MENGGUNAKAN ALGORITMA NAÏVE BAYES (STUDI KASUS: TIMNAS INDONESIA SENIOR, U-23, DAN U-19),” Seinasi-Kesi, pp. 1–8, 2021, [Online]. Available: https://conference.upnvj.ac.id/index.php/seinasikesi/article/view/1909.

M. Hadiyan Sidik, S. Widiyanesti, and D. Puteri Ramadhani, “Analisis Sentimen dan Topic Modelling Terhadap Tim Nasional Indonesia di Kejuaraan AFF Suzuki Cup 2020 Berdasarkan Opini Pengguna Twitter Analysis of Sentiment and Topic Modeling of the Indonesian National Team in the 2020 AFF Suzuki Cup Championship Base,” vol. 9, no. 5, pp. 2783–2796, 2022.

R. N. Melinda, L. M. Ningrum, I. B. Suryabrata, G. S. B. A. Dwipa, and T. P. Sukoco, “Program Perhitungan RAB Pekerjaan Struktur Baja (WF BEAM) Menggunakan Bahasa Python,” TIERS Inf. Technol. J., vol. 2, no. 1, 2021, doi: 10.38043/tiers.v2i1.2838.

V. No, J. Hal, and R. G. Guntara, “Pemanfaatan Google Colab Untuk Aplikasi Pendeteksian Masker Wajah Menggunakan Algoritma Deep Learning YOLOv7 Jurnal Teknologi Dan Sistem Informasi Bisnis,” vol. 5, no. 1, pp. 55–60, 2023.

K. M. L. Muhammad Fadli Asshiddiqi, “Perbandingan Metode Decision Tree dan Support Vector Machine untuk Analisis Sentimen pada Instagram Mengenai Kinerja PSSI,” vol. 7, no. 3, pp. 5–6, 2020.

E. R. Setyaningsih, “Sentiment Classification untuk Opini Berita SepakBola,” J. Intell. Syst. Comput., vol. 3, no. 2, pp. 93–98, 2021, doi: 10.52985/insyst.v3i2.193.




DOI: http://dx.doi.org/10.26798/jiss.v2i2.1136

Article Metrics

Abstract view : 313 times
PDF - 187 times

Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 Yuli Astuti


JOURNAL OF INTELLIGENT SOFTWARE SYSTEMS

Published by

Magister Teknologi Informasi
Lembaga Penelitian dan Pengabdian Masyarakat

Universitas Teknologi Digital Indonesia (d.h STMIK AKAKOM)
Jl. Raya Janti Jl. Majapahit No.143, Jaranan, Banguntapan,
Kec. Banguntapan, Kabupaten Bantul,
Daerah Istimewa Yogyakarta 55918

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.