ANALYSIS OF PUBLIC SENTIMENT RELATED TO THE FAILURE OF INDONESIA TO HOST U-20 USING MULTINOMIAL NAÏVE BAYES CLASSIFIER

Fachri Zaini; Jessica Windi  Sari; Firman Noor Hasan

doi:10.52436/1.jutif.2023.4.6.1209

Fachri Zaini Informatics Engineering Department, Faculty of Engineering, Prof. Dr. HAMKA Muhammadiyah University, Indonesia
Jessica Windi Sari Psychology Department, Faculty of behavioral science, HELP University, Malaysia
Firman Noor Hasan Informatics Engineering Department, Faculty of Engineering, Prof. Dr. HAMKA Muhammadiyah University, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2023.4.6.1209

Keywords: Naive bayes, Sentiment analysis, World cup U-20

Abstract

The case of Indonesia's failure to host the U-20 World Cup in 2023 has become a hot topic of discussion in Indonesia. The rejection of the Israel U-20 national team and security factors by FIFA are considered the main reasons for the cancellation. This raises many issues and controversies from various parties. In this study, sentiment analysis using the Naive Bayes algorithm was conducted. Researchers use the naive bayes algorithm because this algorithm has high accuracy with simple calculations. The data obtained in this study came from 250 tweets of Twitter data with a ratio of training and test data of 7:3. The results showed good data classification with 97.26% accuracy, 93.33% precision, and 100% recall. In conclusion, the classification model developed can describe public sentiment related to Indonesia's failure in the U-20 World Cup well.

Downloads

Download data is not yet available.

References

F. N. Hasan and M. Dwijayanti, “Analisis Sentimen Ulasan Pelanggan Terhadap Layanan Grab Indonesia Menggunakan Multinominal Naïve Bayes Classifier,” J. Linguist. Komputasional, vol. 4, no. 2, pp. 52–58, 2021, doi: https://doi.org/10.26418/jlk.v4i2.61.

A. L. Fairuz, R. D. Ramadhani, and N. A. F. Tanjung, “Analisis Sentimen Masyarakat Terhadap COVID-19 Pada Media Sosial Twitter,” J. Dinda Data Sci. Inf. Technol. Data Anal., vol. 1, no. 1, pp. 42–51, 2021, doi: 10.20895/dinda.v1i1.180.

S. N. J. Fitriyyah, N. Safriadi, and E. E. Pratama, “Analisis Sentimen Calon Presiden Indonesia 2019 dari Media Sosial Twitter Menggunakan Metode Naive Bayes,” J. Edukasi dan Penelit. Inform., vol. 5, no. 3, p. 279, 2019, doi: 10.26418/jp.v5i3.34368.

E. Nofianto, Fitriyah, and Supratiwi, “Media Sosial sebagai Sarana Pendidikan Politik oleh Pejabat Publik (Studi pada Akun Media Sosial Nur Hidayat Sardini) Eri,” J. Ilm., vol. 10, no. 2, pp. 1–94, 2019, doi: 10.33087/jiubj.v23i1.3060.

A. Rismawan, “Dikritik Gara-gara Batal Piala Dunia U-20 di Indonesia, Ganjar Pranowo : Ini Bukan Kiamat,” bandung.viva.co.id, 2023. [Online]. Available: https://bandung.viva.co.id/bola/17997-dikritik-gara-gara-batal-piala-dunia-u-20-di-indonesia-ganjar-pranowo-ini-bukan-kiamat.

B. Baskoro, “Paradoks Indonesia Saat Batal Jadi Tuan Rumah Piala Dunia U-20 2023,” sport.detik, 2023. [Online]. Available: https://sport.detik.com/sepakbola/liga-indonesia/d-6651105/paradoks-indonesia-saat-batal-jadi-tuan-rumah-piala-dunia-u-20-2023.

Alfandi Safira and F. N. Hasan, “Analisis Sentimen Masyarakat Terhadap Paylater Menggunakan Metode Naive Bayes Classifier,” Zo. J. Sist. Inf., vol. 5, no. 1, pp. 59–70, 2023, doi: 10.31849/zn.v5i1.12856.

I. R. Afandi, I. F. Hanif, F. N. Hasan, E. Sinduningrum, Z. Halim, and N. Pratiwi, “Analisis Sentimen Opini Masyarakat Terkait Penyelenggaraan Sistem Elektronik Menggunakan Metode Logistic Regression,” J. Linguist. Komputasional, vol. 5, no. 2, pp. 77–84, 2022, doi: https://doi.org/10.26418/jlk.v5i2.103.

M. Siddik, Hendri, R. N. Putri, and Y. Desnelita, “Klasifikasi Kepuasan Mahasiswa Terhadap Pelayanan Perguruan Tinggi Menggunakan Algoritma Naïve Bayes Classification,” vol. 3, pp. 1–23, 2020, doi: 10.31539/intecoms.v3i2.1654.

R. Sari and R. Y. Hayuningtyas, “Penerapan Algoritma Naive Bayes Untuk Analisis Sentimen Pada Wisata TMII Berbasis Website,” Indones. J. Softw. Eng., vol. 5, no. 2, pp. 51–60, 2019, doi: 10.31294/ijse.v5i2.6957.

E. Indrayuni, “Klasifikasi Text Mining Review Produk Kosmetik Untuk Teks Bahasa Indonesia Menggunakan Algoritma Naive Bayes,” J. Khatulistiwa Inform., vol. 7, no. 1, pp. 29–36, 2019, doi: 10.31294/jki.v7i1.5740.

C. M. S. Ramdani, A. N. Rachman, and R. Setiawan, “Comparison of the Multinomial Naive Bayes Algorithm and Decision Tree with the Application of AdaBoost in Sentiment Analysis Reviews PeduliLindungi Application,” Int. J. Inf. Syst. Technol. Akreditasi, vol. 6, no. 4, pp. 419–430, 2022.

A. A. Farisi, Y. Sibaroni, and S. Al Faraby, “Sentiment analysis on hotel reviews using Multinomial Naïve Bayes classifier,” J. Phys. Conf. Ser., vol. 1192, no. 1, 2019, doi: 10.1088/1742-6596/1192/1/012024.

L. Hermawan and M. Bellaniar Ismiati, “Pembelajaran Text Preprocessing berbasis Simulator Untuk Mata Kuliah Information Retrieval,” J. Transform., vol. 17, no. 2, p. 188, 2020, doi: 10.26623/transformatika.v17i2.1705.

M. Syarifuddinn, “Analisis Sentimen Opini Publik Mengenai Covid-19 Pada Twitter Menggunakan Metode Naïve Bayes Dan Knn,” INTI Nusa Mandiri, vol. 15, no. 1, pp. 23–28, 2020, doi: 10.33480/inti.v15i1.1347.

A. Muhammad Iqbal, G. Dudih, and S. Falentino, “Analisis Sentiment Masyarakat terhadap Kasus Covid-19 pada Media Sosial Youtube dengan Metode Naive bayes,” J. Sains Komput. Inform. (J-SAKTI, vol. 5, no. 2, pp. 807–814, 2021, doi: https://dx.doi.org/10.30645/j-sakti.v5i2.378.

M. A. Rofiqi, A. C. Fauzan, A. P. Agustin, and A. A. Saputra, “Implementasi Term-Frequency Inverse Document Frequency (TF-IDF) Untuk Mencari Relevansi Dokumen Berdasarkan Query,” Ilk. J. Comput. Sci. Appl. Informatics, vol. 1, no. 2, pp. 58–64, 2019, doi: 10.28926/ilkomnika.v1i2.18.

A. H. Anshor and A. Safuwan, “Analisis Sentimen Opini Warganet Twitter Terhadap Tes Screening Genose Pendeteksi Virus Covid-19 Menggunakan Metode Naïve Bayes Berbasis Particle Swarm Optimization,” J. Inform. Teknol. dan Sains, vol. 5, no. 1, pp. 170–178, 2023, doi: 10.51401/jinteks.v5i1.2229.

I. R. Afandi, F. N. Hasan, A. A. Rizki, N. Pratiwi, and Z. Halim, “Analisis Sentimen Opini Masyarakat Terkait Pelayanan Jasa Ekspedisi Anteraja Dengan Metode Naive Bayes,” J. Linguist. Komputasional, vol. 5, no. 2, pp. 63–70, 2022, doi: https://doi.org/10.26418/jlk.v5i2.107.

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.

A. N. Kasanah, M. Muladi, and U. Pujianto, “Penerapan Teknik SMOTE untuk Mengatasi Imbalance Class dalam Klasifikasi Objektivitas Berita Online Menggunakan Algoritma KNN,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 3, no. 2, pp. 196–201, 2019, doi: 10.29207/resti.v3i2.945.

R. L. Hasanah, M. Hasan, W. E. Pangesti, F. F. Wati, and W. Gata, “Klasifikasi Penerima Dana Bantuan Desa Menggunakan Metode Knn (K-Nearest Neighbor),” J. Techno Nusa Mandiri, vol. 16, no. 1, pp. 1–6, 2019, doi: 10.33480/techno.v16i1.25.

M. Ismail, N. Hassan, and S. Saleh Bafjaish, “Comparative Analysis of Naive Bayesian Techniques in Health-Related For Classification Task,” J. Soft Comput. Data Min., vol. 1, no. 2, pp. 1–10, 2020.

A. Nugroho, “Analisa Splitting Criteria Pada Decision Tree dan Random Forest untuk Klasifikasi Evaluasi Kendaraan,” JSITIK J. Sist. Inf. dan Teknol. Inf. Komput., vol. 1, no. 1, pp. 41–49, 2022, doi: 10.53624/jsitik.v1i1.154.