Sentiment Analysis of Digital Television Migration on Twitter Using Naïve Bayes Multinomial Comparison, Support Vector Machines, and Logistic Regression Algorithms
DOI:
https://doi.org/10.32736/sisfokom.v12i2.1668Keywords:
Sentiment analysis, Multinomial Naïve bayes, Support Vector Machines, Logistic regression, TwitterAbstract
The Ministry of Communication and Information Technology (KEMENKOMINFO) has announced to the publics in Indonesia regarding the termination of analog television broadcasts or called analog switch-off, which requires the public to migrate from analog television to digital television. Regarding the process of stopping analog broadcasts this raises pros and cons by the people in Indonesia. Many people give their respective opinions through social media, especially on Twitter. A collection of pros and cons data from the public can be collected and used as research of sentiment analysis. This research will focus on comparing three classification algorithms, which is called Multinomial Naïve Bayes, Support Vector Machines, and Logistic Regression using the same dataset and the same method called Lexicon Based. The results showed that the highest accuracy is Support Vector Machines with the accuracy is 94.00%, Logistic Regression with the accuracy is 90.00%, and Multinomial Naïve Bayes with the accuracy is 88.00%.References
M. Oktaviana, Z. A. Achmad, H. Arviani, and K. Kusnarto, “Budaya Komunikasi Virtual di Twitter dan Tiktok: Perluasan Makna Kata Estetik,” Satwika: Kajian Ilmu Budaya dan Perubahan Sosial, vol. 5, no. 2, pp. 173-186. 2021.
M. Alaydrus, “Digital Dividend pada Migrasi TV Analog ke TV Digital–Prospek dan Dilema,” . InComTech: Jurnal Telekomunikasi dan Komputer, vol. 1, no. 1, pp. 1-9. 2009.
M. Firdaus, “Analisis Inovasi Industri Televisi Menghadapi Migrasi Televisi Digital (Studi Kasus Televisi Lokal Di Provinsi Bengkulu),” In Conference on Economic and Business Innovation (CEBI), pp. 783-800. 2021.
S. Budhirianto, “Sikap Masyarakat terhadap Kampanye Televisi Digital pada Media Televisi,” Jurnal Penelitian Komunikasi Dan Opini Publik, vol. 18, no. 3. 2014.
V. Zuliana, G. Garno, and I. Maulana, “Analisis Sentimen Program Migrasi TV Digital Menggunakan Algoritma Naive Bayes dengan Chi Square,” Jurnal Informasi dan Komputer, vol. 10, no. 2, pp. 90-95. 2022.
S. Tiwari, A. Verma., P. Garg., and D. Bansal, “Social Media Sentiment Analysis on Twitter Datasets,” In 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), pp. 925-927. 2020.
A. Shelar, and C. Y. Huang, “Sentiment Analysis of Twitter Data,” In 2018 International Conference on Computational Science and Computational Intelligence (CSCI), pp. 1301-1302. 2018.
C. Kariya, and P. Khodke, “Twitter Sentiment Analysis,” In 2020 International Conference for Emerging Technology (INCET), pp. 1-3. 2020.
L. Pasteur, and R. Koch, “Mining Twitter Data on COVID-19 for Sentiment Analysis and Frequent Patterns Discovery Habiba,” Algiers Univ. vol. 74, no. 1934, pp. 1-3, 2020.
S. Tam, R. B. Said., and Ö. Ö. Tanriöver, “A ConvBiLSTM Deep Learning Model-Based Approach for Twitter Sentiment Classification,” pp. 41283-41293. 2021.
F. F. Rachman, and S. Pramana, “Analisis Sentimen Pro dan Kontra Masyarakat Indonesia Tentang Vaksin COVID-19 pada Media Sosial Twitter,” Indonesian of Health Information Management Journal, vol. 8, no. 2, pp. 100-109. 2020.
Yuyun, N. Hidayah, and S. Sahibu, “Algoritma Multinomial Naïve Bayes Untuk Klasifikasi Sentimen Pemerintah Terhadap Penanganan COVID-19 Menggunakan Data Twitter,” Jurnal Resti (Rekayasa Sistem dan Teknologi Informasi, vol. 5, no. 4, pp. 820-826. 2021.
N. A. Susanti, M. Walid, and H. Hoiriyah, “Klasifikasi Data Tweet Ujaran Kebencian Di Media Sosial Menggunakan Naive Bayes Classifier”, JATI (Jurnal Mahasiswa Teknik Informatika), vol. 6, no. 2, pp. 538-543. 2022.
P. Arsi, and R. Waluyo, “Analisis Sentimen Wacana Pemindahan Ibu Kota Indonesia Menggunakan Algoritma Support Vector Machine (SVM),” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 8, no. 1, pp. 147. 2021.
D. A. Ramadhan, and E. B. Setiawan, “Analisis Sentimen Program Acara di SCTV pada Twitter Menggunakan Metode Naive Bayes dan Support Vector Machine,” eProceedings of Engineering, vol. 6, no. 2. 2019.
A. K. Santoso, A, Noviriandini, A. Kurniasih, B. Wicaksono, and A. Nuryanto, “Klasifikasi Persepsi Pengguna Twitter Terhadap Kasus COVID-19 menggunakan Metode Logistic Regression,” Jurnal Informatika Kaputama (JIK), vol. 5, no. 2, pp. 234-241. 2021.
A. Novantika, and S. Sugiman, “Analisis Sentimen Ulasan Pengguna Aplikasi Video Conference Google Meet menggunakan Metode SVM dan Logistic Regression,” In PRISMA, Prosiding Seminar Nasional Matematika, vol. 5, pp. 808-813. 2022.
T. Wulandari, Klasifikasi Jenis Emosi dari Tweet Berbahasa Indonesia Menggunakan Metode Support Vector Machine. Doctoral Dissertation, Universitas Islam Negeri Sultan Syarif Kasim: Riau, 2018.
M. W. Putri, A. Muchayan, and M. Kamisutara, “Sistem Rekomendasi Produk Pena Eksklusif Menggunakan Content-Based Filtering dan TF-IDF,” Journal of Information Technology and Computer Science, vol. 5, no. 3, pp. 229-236. 2020.
F. Ariani, and A. Taufik, “Perbandingan Metode Klasifikasi Data Mining untuk Prediksi Tingkat Kepuasan Pelanggan Telkomsel Prabayar,” SATIN-Sains dan Teknologi Informasi, vol. 6, no. 2, pp. 46-55. 2020.
K. Kelvin, J. Banjarnahor, M. N. Nababan, S. H. Sinurat, “Analisis Perbandingan Sentimen Corona Virus Disease-2019 (COVID19) pada Twitter Menggunakan Metode Logistic Regression dan Support Vector Machine,” Jurnal Sistem Informasi dan Ilmu Komputer Prima, vol. 5, no. 2, pp. 47-52. 2022.
A. Bimantara, and T. A. Dina, “Klasifikasi Web Berbahaya Menggunakan Metode Logistic Regression,” In Annual Research Seminar (ARS), vol. 4, no. 1, pp. 173-177. 2019.
A. R. Rizkirobby, M. Nasrun, and R. A. Nugrahaeni, “Deteksi Ujaran Ancaman Berbasis Website pada Postingan Media Sosial Twitter Menggunakan Metode Naïve Bayes,” eProceedings of Engineering, vol. 8, no. 1. 2021.
W. Willianto, I. A. Musdar, J. Junaedy, and H. Angriani, “Implementasi Teori Naive Bayes dalam Klasifikasi Ujaran Kebencian di Facebook,” Jurnal Informatika Universitas Pamulang, vol. 6, no. 4, pp. 666-671. 2021.
A. S. Maulana, Klasifikasi Ujaran Kebencian dan Bahasa Kasar di Twitter Menggunakan Metode Attention Based Recurrent Neural Network. Doctoral Dissertation UPN “Veteran Yogyakarta”. Yogyakarta: 2022.
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