Analisis Sentimen Respon Publik Terhadap Program Internet Gratis di Platform X Melalui Pendekatan Algoritma Naïve Bayes

Muhammad Rizky Julianto; Yuma Akbar; Tri Wahyudi

doi:10.35870/jimik.v5i3.981

Vol. 5 No. 3 (2024) • Articles

Open Access

Analisis Sentimen Respon Publik Terhadap Program Internet Gratis di Platform X Melalui Pendekatan Algoritma Naïve Bayes

Muhammad Rizky Julianto

Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika

Yuma Akbar

Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika

Tri Wahyudi

Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika

DOI: https://doi.org/10.35870/jimik.v5i3.981

Published: September 20, 2024 Pages: 2940-2950

Original Full-Text Article

Download published version for reading and archiving

Download PDF

Abstract

The free internet program was launched in response to the public's need for affordable internet access. Digital transformation relies on four main pillars, including infrastructure, government, digital society, and the economy. In addition, connectivity is also an important component. Indonesia is one of the countries where many people are not connected to the internet. We Are Social reported that there are 63.51 million people throughout the country who have not been connected to the internet until early 2023. The number is the eighth largest globally. After the acceleration of ICT infrastructure development, there is a need for a free internet program initiated by the government or certain organizations that aims to provide wider and more equitable internet access, especially to students and the general public, especially in areas that have not yet received internet access. Although free internet programs are very beneficial, it is undeniable that there are a number of negative impacts that need to be considered. It is important to be aware of and overcome its negative impact. It is necessary to Sentiment Analysis of free internet programs to find out how much positive and negative responses there are in this program with the Naive Bayes algorithm approach so that the need for free internet programs can be realized.

Keywords:

Internet Program Response Naive Bayes

Article Metrics & Downloads Graph

Monthly Download Trends:

Author Biographies

Muhammad Rizky Julianto Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika

Program Studi Sistem Informasi, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia.

Yuma Akbar Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika

Program Studi Sistem Informasi, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia.

Tri Wahyudi Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika

Program Studi Sistem Informasi, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia.

How to Cite

Julianto, M. R., Akbar, Y., & Wahyudi, T. (2024). Analisis Sentimen Respon Publik Terhadap Program Internet Gratis di Platform X Melalui Pendekatan Algoritma Naïve Bayes. Jurnal Indonesia : Manajemen Informatika Dan Komunikasi, 5(3), 2940-2950. https://doi.org/10.35870/jimik.v5i3.981

License

Creative Commons Attribution 4.0 International License (CC BY 4.0)

This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License .

Share: You are free to copy, distribute, and transmit the work in any medium or format.
Adapt: You are free to remix, transform, and build upon the work for any purpose, even commercially.
Attribution: You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.

Copyright & Retention: Authors retain copyright without restrictions and grant this journal the right of first publication under an open-access model. The journal retains non-exclusive publishing rights for archiving, indexing, and scholarly dissemination.

References

Total: 25 References

Adisel, A., & Prananosa, A. G. (2020). Penggunaan teknologi informasi dan komunikasi dalam sistem manajemen pembelajaran pada masa pandemi Covid 19. Journal Of Administration and Educational Management (ALIGNMENT), 3(1), 1-10. DOI: https://doi.org/10.31539/alignment.v3i1.1291.
Al Fachri, M. A., & Athiyah, U. (2023). Komparasi Model Analisis Sentimen Pada Twitter Terhadap Kemahalan Minyak Goreng dengan Metode Naive Bayes dan Support Vector Machine. Infotekmesin, 14(2), 194-199.
Alita, D., & Shodiqin, R. A. (2023). Sentimen Analisis Vaksin Covid-19 Menggunakan Naive Bayes Dan Support Vector Machine. Journal of Artificial Intelligence and Technology Information, 1(1), 1-12. DOI: https://doi.org/10.58602/jaiti.v1i1.20.
Anbari, M. Z., & Sugiantoro, B. (2023). Studi Komparasi Metode Analisis Sentimen Naïve Bayes, SVM, dan Logistic Regression Pada Piala Dunia 2022. Jurnal Media Informatika Budidarma, 7(2), 688-695. DOI: http://dx.doi.org/10.30865/mib.v7i2.5383.
Arischo, R. S., & Damayanti, D. (2024). Analisis Sentimen Pinjaman Online di Twitter dengan Metode Naive Bayes Classifier dan SVM. JURNAL MEDIA INFORMATIKA BUDIDARMA, 8(2), 1120-1130. DOI: http://dx.doi.org/10.30865/mib.v8i2.7406.
Effendi, L., & Syukmana, F. (2022). Analisa sentimen vaksinasi covid-19 dengan metode support vector machine dan naïve bayes berbasis teknik SMOTE.
Fikri, M. I., Sabrila, T. S., & Azhar, Y. (2020). Perbandingan metode naïve bayes dan support vector machine pada analisis sentimen twitter. SMATIKA Jurnal: STIKI Informatika Jurnal, 10(02), 71-76.
Handayanto, R. T., Herlawati, H., Atika, P. D., Khasanah, F. N., Yusuf, A. Y. P., & Septia, D. Y. (2021). Analisis Sentimen Pada Situs Google Review dengan Naïve Bayes dan Support Vector Machine. Jurnal Komtika (Komputasi dan Informatika), 5(2), 153-163. DOI: https://orcid.org/0000-0001-9433-3882.
Indriyani, F. A., Fauzi, A., & Faisal, S. (2023). Analisis sentimen aplikasi tiktok menggunakan algoritma naïve bayes dan support vector machine. TEKNOSAINS: Jurnal Sains, Teknologi dan Informatika, 10(2), 176-184. DOI: https://doi.org/10.37373/tekno.v10i2.419.
Kurniawan, I., Hananto, A. L., Hilabi, S. S., Hananto, A., Priyatna, B., & Rahman, A. Y. (2023). Perbandingan Algoritma Naive Bayes Dan SVM Dalam Sentimen Analisis Marketplace Pada Twitter. JATISI (Jurnal Teknik Informatika Dan Sistem Informasi), 10(1), 731-740. DOI: https://doi.org/10.35957/jatisi.v10i1.3582.
Kusmira, M. (2019). Analisis Sentimen Registrasi Ulang Kartu SIM Pada Twitter Menggunakan Algoritma SVM dan K-NN. Inti Nusa Mandiri, 14(1), 105-110.
Normawati, D., & Prayogi, S. A. (2021). Implementasi Naïve Bayes Classifier Dan Confusion Matrix Pada Analisis Sentimen Berbasis Teks Pada Twitter. J-SAKTI (Jurnal Sains Komputer dan Informatika), 5(2), 697-711. DOI: http://dx.doi.org/10.30645/j-sakti.v5i2.369.
Noviana, R., & Rasal, I. (2023). Penerapan Algoritma Naive Bayes Dan Svm Untuk Analisis Sentimen Boy Band Bts Pada Media Sosial Twitter. Jurnal Teknik dan Science, 2(2), 51-60. DOI: https://doi.org/10.56127/jts.v2i2.791.
Oktavia, D., Ramadahan, Y. R., & Minarto, M. (2023). Analisis Sentimen Terhadap Penerapan Sistem E-Tilang Pada Media Sosial Twitter Menggunakan Algoritma Support Vector Machine (SVM). KLIK: Kajian Ilmiah Informatika dan Komputer, 4(1), 407-417. DOI: https://doi.org/10.30865/klik.v4i1.1040.
Paryono, T., Arfani, M. R., Hananto, A., & Huda, B. (2023). Seleksi Penerimaan Bantuan Internet Gratis dengan Menggunakan Metode AHP. INTERNAL (Information System Journal), 6(1), 1-9. DOI: https://doi.org/10.32627/internal.v6i1.736.
Raharjo, R. A., Sunarya, I. M. G., & Divayana, D. G. H. (2022). Perbandingan Metode Naïve Bayes Classifier Dan Support Vector Machine Pada Kasus Analisis Sentimen Terhadap Data Vaksin Covid-19 Di Twitter. Elkom: Jurnal Elektronika dan Komputer, 15(2), 456-464. DOI: https://doi.org/10.51903/elkom.v15i2.918.
Rasidi, M. A., Hikmatullah, N., & Sobry, M. (2021). Hambatan guru dalam pembelajaran daring: Studi kasus di kelas V MIN 2 Kota Mataram. Jurnal Ilmiah Pendidikan Dasar, 8(2), 159-174. DOI: http://dx.doi.org/10.30659/pendas.8.2.159-174.
Rasyiidin, M. Y. B., Hermawan, I., Kurniawan, A., Murad, F. A., Agustin, M., & Arnaldy, D. (2022). Akses internet gratis untuk usaha mikro kecil dan menengah menggunakan teknologi wireless manajemen channel. Bubungan Tinggi: Jurnal Pengabdian Masyarakat, 4(1), 211-219.
Sari, D. P. EVALUASI PROGRAM INTERNET GRATIS KELURAHAN DALAM MENDUKUNG e-LEARNING DAN e-MARKETING DI KOTA CILEGON.
Sarimole, F. M., & Ihsan, A. N. (2023). Analisis Sentimen Twitter Terhadap Uu Cipta Kerja Dengan Menggunakan Algoritma Naïve Bayes Dan Support Vector Machine. INTECOMS: Journal of Information Technology and Computer Science, 6(2), 822-829. DOI: https://doi.org/10.31539/intecoms.v6i2.7671.
Setiawan, H., Utami, E., & Sudarmawan, S. (2021). Analisis Sentimen Twitter Kuliah Online Pasca Covid-19 Menggunakan Algoritma Support Vector Machine dan Naive Bayes. Jurnal Komtika (Komputasi dan Informatika), 5(1), 43-51. DOI: https://doi.org/10.31603/komtika.v5i1.5189.
Syahda, A. P. A., & Handayani, A. (2024). IMPLEMENTASI PROGRAM WIFI GRATIS UNTUK RUANG TERBUKA PUBLIK DI KOTA BANDAR LAMPUNG. Triwikrama: Jurnal Ilmu Sosial, 3(6), 1-10.
Widyanto, T., Ristiana, I., & Wibowo, A. (2023). Komparasi Naïve Bayes dan SVM Analisis Sentimen RUU Kesehatan di Twitter. SINTECH (Science and Information Technology) Journal, 6(3), 147-161. DOI: https://doi.org/10.31598/sintechjournal.v6i3.1433.
Wulandari, S., & Hasan, F. N. (2024). Analisis Sentimen Masyarakat Indonesia Terhadap Pengalaman Belanja Thrifting Pada Media Sosial Twitter Menggunakan Algoritma Naïve Bayes. Jurnal Media Informatika Budidarma, 8(2), 768-776. DOI: http://dx.doi.org/10.30865/mib.v8i2.7520.
Zelina, N., & Afiyati, A. (2024). Analisis Sentimen Ulasan Pengguna Aplikasi M-Banking Menggunakan Algoritma Support Vector Machine dan Decision Tree. Jurnal Linguistik Komputasional, 7(1), 31-37. DOI: https://doi.org/10.26418/jlk.v7i1.169.