Sentiment Analysis of Twitter Cases of Riots at Kanjuruhan Stadium Using the Naive Bayes Method

Bryan Jerremia Katiandhago; Akhmad Mustolih; Wachyu Dwi Susanto; Pungkas Subarkah; Chendri Irawan Satrio Nugroho

doi:10.47709/cnahpc.v5i1.2196

Authors

Bryan Jerremia Katiandhago Universitas Amikom Purwokerto
Akhmad Mustolih Universitas Amikom Purwokerto
Wachyu Dwi Susanto Universitas Amikom Purwokerto
Pungkas Subarkah Universitas Amikom Purwokerto
Chendri Irawan Satrio Nugroho Institut Teknologi Tangerang Selatan

DOI:

https://doi.org/10.47709/cnahpc.v5i1.2196

Keywords:

sentiment, sentiment analysis, Kanjuruhan, Naive Bayes, Twitter

Abstract

Sentiment analysis is a process carried out to analyze opinions, sentiments, judgments, and emotions from the riot case at the kanjuruhan stadium. The purpose of this research is to find out public opinion about the tragedy that is currently happening at the Kanjuruhan Stadium. the data was obtained from social media Twitter using the Twitter API, then after that, an analysis was carried out. data from the results of the analysis will be classified using the Naive Bayes method. The classification process is divided into 7 (seven) stages, namely Crawling, Cleansing, pre-processing, labeling, classification, data training, and data testing. In the labeling process, data is classified into 2 (two) classes, namely the positive class and the negative class. The data obtained before the preprocessing process was 1963 tweets, after the preprocessing the data obtained was 1001 tweets. The data will be trained and tested using the naive Bayes classification method. classification results obtained precision values of 82% for negative data and 65% for positive data, recall values obtained 74% for negative data and 75% for positive data, F1-score values obtained 78% for negative data and 70% for positive data, while accuracy value obtained 74%.

Downloads

Download data is not yet available.

References

Alita, D., & Isnain, A. R. (2020). Pendeteksian Sarkasme pada Proses Analisis Sentimen Menggunakan Random Forest Classifier. Jurnal Komputasi, 8(2), 50–58. https://doi.org/10.23960/komputasi.v8i2.2615

Andika, L. A., Azizah, P. A. N., & Respatiwulan, R. (2019). Analisis Sentimen Masyarakat terhadap Hasil Quick Count Pemilihan Presiden Indonesia 2019 pada Media Sosial Twitter Menggunakan Metode Naive Bayes Classifier. Indonesian Journal of Applied Statistics, 2(1), 34. https://doi.org/10.13057/ijas.v2i1.29998

Ardiani, L., Sujaini, H., & Tursina, T. (2020). Implementasi Sentiment Analysis Tanggapan Masyarakat Terhadap Pembangunan di Kota Pontianak. Jurnal Sistem Dan Teknologi Informasi (Justin), 8(2), 183. https://doi.org/10.26418/justin.v8i2.36776

Dharmawan, L. R., Arwani, I., & Ratnawati, D. E. (2020). Analisis Sentimen pada Sosial Media Twitter Terhadap Layanan Sistem Informasi Akademik Mahasiswa Universitas Brawijaya dengan Metode K- Nearest Neighbor. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 4(3), 959–965. Retrieved from http://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/7099

Fairuz, A. L., Ramadhani, R. D., & Tanjung, N. A. F. (2021). Analisis Sentimen Masyarakat Terhadap COVID-19 Pada Media Sosial Twitter. Journal of Dinda?: Data Science, Information Technology, and Data Analytics, 1(1), 42–51. https://doi.org/10.20895/dinda.v1i1.180

Fikri, M. I., Sabrila, T. S., & Azhar, Y. (2020). Perbandingan Metode Naïve Bayes dan Support Vector Machine pada Analisis Sentimen Twitter. Smatika Jurnal, 10(02), 71–76. https://doi.org/10.32664/smatika.v10i02.455

Fitri, E. (2020). Analisis Sentimen Terhadap Aplikasi Ruangguru Menggunakan Algoritma Naive Bayes, Random Forest Dan Support Vector Machine. Jurnal Transformatika, 18(1), 71. https://doi.org/10.26623/transformatika.v18i1.2317

Gunawan, B., Pratiwi, H. S., & Pratama, E. E. (2018). Sistem Analisis Sentimen pada Ulasan Produk Menggunakan Metode Naive Bayes. Jurnal Edukasi Dan Penelitian Informatika (JEPIN), 4(2), 113. https://doi.org/10.26418/jp.v4i2.27526

Gunawan, R., Septiadi, R., Apri Wenando, F., Mukhtar, H., & Syahril. (2022). K-Nearest Neighbor (KNN) untuk Menganalisis Sentimen terhadap Kebijakan Merdeka Belajar Kampus Merdeka pada Komentar Twitter. Jurnal CoSciTech (Computer Science and Information Technology), 3(2), 152–158. https://doi.org/10.37859/coscitech.v3i2.3841

Kurniawan, R., & Apriliani, A. (2020). 13686-34825-1-SM.pdf. Analisis Sentimen Masyarakat Terhadap Virus Corona Berdasarkan Opini Dari Twitter Berbasis Web Scraper, Vol. 5.

Muzaki, A., & Witanti, A. (2021). Sentiment Analysis of the Community in the Twitter To the 2020 Election in Pandemic Covid-19 By Method Naive Bayes Classifier. Jurnal Teknik Informatika (Jutif), 2(2), 101–107. https://doi.org/10.20884/1.jutif.2021.2.2.51

Natasuwarna, A. P. (2019). Analisis Sentimen Keputusan Pemindahan Ibukota Negara Menggunakan Klasifikasi Naive Bayes. SEMINAR NASIONAL SISTEM INFORMASI Dan TEKNIK INFORMATIKA SENSITIF, 47–53.

Puspita, R., & Widodo, A. (2021). Perbandingan Metode KNN, Decision Tree, dan Naïve Bayes Terhadap Analisis Sentimen Pengguna Layanan BPJS. Jurnal Informatika Universitas Pamulang, 5(4), 646. https://doi.org/10.32493/informatika.v5i4.7622

Sari, F. V., & Wibowo, A. (2019). Analisis Sentimen Pelanggan Toko Online Jd.Id Menggunakan Metode Naïve Bayes Classifier Berbasis Konversi Ikon Emosi. Jurnal SIMETRIS, 10(2), 681–686.

Syarifuddinn, M. (2020). Analisis Sentimen Opini Publik Mengenai Covid-19 Pada Twitter Menggunakan Metode Naïve Bayes Dan Knn. INTI Nusa Mandiri, 15(1), 23–28. https://doi.org/10.33480/inti.v15i1.1347