Analisis Sentimen Tanggapan Publik di Twitter Terkait Program Kerja Makan Siang Gratis Prabowo–Gibran Menggunakan Algoritma Naïve Bayes Classifier dan Support Vector Machine


  • Annisa Ramadhani * Mail Universitas Islam Negeri Sultan Syarif Kasim, Riau, Indonesia
  • Inggih Permana Universitas Islam Negeri Sultan Syarif Kasim, Riau, Indonesia
  • M Afdal Universitas Islam Negeri Sultan Syarif Kasim, Riau, Indonesia
  • Mona Fronita Universitas Islam Negeri Sultan Syarif Kasim, Riau, Indonesia
  • (*) Corresponding Author
Keywords: Stunting; Free Lunch Program; Sentiment Analysis; Naïve Bayes Classifier; Support Vector Machine

Abstract

Indonesia faces a serious challenge related to stunting, with rates reaching 21% in 2024, although this represents a decrease from 24% in 2021. In response, the government has launched various programs to address this issue, including nutrition education, health check-ups for pregnant women, and supplementary food provisions. Amid these efforts, the proposed free lunch program aims to improve nutritional quality for children and pregnant women. However, this program has sparked controversy over the required budget, estimated at IDR 450 trillion, which could impact the national budget balance and lead to inflation.
This study analyzes public sentiment toward the free lunch program using the Naïve Bayes Classifier (NBC) and Support Vector Machine (SVM) algorithms. An analysis of 1,028 tweets revealed that negative sentiment predominates at 44.84%, followed by positive sentiment (32.39%) and neutral sentiment (22.76%). SVM outperformed NBC with an accuracy of 75.39%, compared to NBC's 68.97%. The findings provide important insights into public perceptions of the program and highlight the need for further research to improve sentiment analysis methodologies.

Downloads

Download data is not yet available.

References

F. H. S. Rio Mahesa Putra, “Ciherang Stunting Corner: A step to reduce the prevalence of stunting,” J. Community Serv., vol. 2, pp. 335–348, 2023.

S. Sulistyowati et al., “Analisis Faktor Penyebab Stunting dan Pembagian Gizi Ringan sebagai Upaya Pemenuhan Gizi di Kelurahan Marang Bawah,” J. Kreat. Pengabdi. Kpd. Masy., vol. 6, no. 12, pp. 5564–5582, 2023, doi: 10.33024/jkpm.v6i12.12602.

M. E. D. Vanti, V. Octaviani, and M. Maryaningsih, “Analisis Framing Pemberitaan Program Makan Gratis Prabowo Subianto Di Media Online,” Prof. J. Komun. dan Adm. Publik, vol. 11, no. 1, pp. 427–436, 2024, doi: 10.37676/professional.v11i1.6396.

Y. Hidaytillah et al., “Pemberdayaan Masyarakat untuk Pencegahan Stunting dalam Rangka Membangun Masa Depan Masyarakat Unggul,” Welf. J. Pengabdi. Masy., vol. 1, no. 4, pp. 657–661, 2023, [Online]. Available: https://jurnalfebi.iainkediri.ac.id/index.php/Welfare

R. Saputra and F. N. Hasan, “Analisis Sentimen Terhadap Program Makan Siang & Susu Gratis Menggunakan Algoritma Naive Bayes,” J. Teknol. Dan Sist. Inf. Bisnis, vol. 6, no. 3, pp. 411–419, 2024, doi: 10.47233/jteksis.v6i3.1378.

D. N. R. Tundo, “Implementasi Algoritma Naive Bayes untuk Analisis Sentimen Terhadap Program Makan Siang Gratis,” J. Indones. Manaj. Inform. dan Komun., vol. 5, no. 3, 2024, [Online]. Available: https://journal.stmiki.ac.id/index.php/jimik/article/view/978/770

Z. Purwanti, “Pemodelan Text Mining untuk Analisis Sentimen Terhadap Program Makan Siang Gratis di Media Sosial X Menggunakan Algoritma Support Vector Machine ( SVM ),” vol. 5, no. 3, pp. 3065–3079, 2024.

A. Sitanggang, R. I. Umaidah, Yuyun, and Adam, “Analisis Sentimen Masyarakat Terhadap Program Makan Siang Gratis Pada Media Sosial X Menggunakan Algoritma Naïve Bayes,” J. Inform. dan Tek. Elektro Terap., vol. 12, no. 3, 2024, doi: 10.23960/jitet.v12i3.4902.

M. A. S. Putra, I. Permana, M. Mustakim, and M. Afdal, “Analisis Sentimen Masyarakat Mengenai Gerakan Childfree di Media Sosial X Menggunakan Algoritma NBC dan SVM,” MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 4, no. 4, pp. 1189–1198, 2024, doi: 10.57152/malcom.v4i4.1356.

R. Afandi, M. Afdal, and R. Novita, “Analisis Sentimen Masyarakat Terhadap Pinjaman Online di Twitter Menggunakan Algoritma Naïve Bayes Classifier dan K-Nearest N eighbor,” vol. 6, no. 2, pp. 596–605, 2024, doi: 10.47065/bits.v6i2.5300.

R. N. Yudistira Arya Wibisono, M. Afdal, Mustakim, “Implementasi Algoritma Support Vector Machine Untuk Analisa Sentimen Data Ulasan Aplikasi Pinjaman Online di Google Play Store,” MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 4, no. 4, pp. 1244–1252, 2024, doi: 10.57152/malcom.v4i4.1435.

Y. A. Putri, S. Defit, and G. W. Nurcahyo, “Analisis Sentimen Publik Terhadap Program Penurunan Angka Prevalensi Stunting Indonesia Menggunakan Data Twitter Dengan Metode Naïve Bayes,” vol. 4, pp. 1978–1989, 2024.

D. A. Warraihan, I. Permana, M. Mustakim, and R. Novita, “Analisis Sentimen Pengguna Transportasi Online Maxim Pada Instagram Menggunakan Naïve Bayes Classifier dan K-Nearest Neighbor,” J. Media Inform. Budidarma, vol. 7, no. 3, p. 1134, 2023, doi: 10.30865/mib.v7i3.6336.

I. P. Rahayu, A. Fauzi, and J. Indra, “Analisis Sentimen Terhadap Program Kampus Merdeka Menggunakan Naive Bayes Dan Support Vector Machine,” J. Sist. Komput. dan Inform., vol. 4, no. 2, p. 296, 2022, doi: 10.30865/json.v4i2.5381.

G. Ginabila and A. Fauzi, “Analisis Sentimen Terhadap Pemutar Musik Online Spotify Dengan Algoritma Naive Bayes dan Support Vector Machine,” J. Ilm. Ilk. - Ilmu Komput. Inform., vol. 6, no. 2, pp. 111–122, 2023, doi: 10.47324/ilkominfo.v6i2.180.

E. S. Romaito, M. K. Anam, Rahmaddeni, Ulfah, and A. Noviciate, “Perbandingan Algoritma SVM Dan NBC Dalam Analisa Sentimen Pilkada Pada Twitter,” pp. 169–179, 2021, [Online]. Available: https://doi.org/10.22303/csrid.13.3.2021.169-179

D. Ananda and R. R. Suryono, “Analisis Sentimen Publik Terhadap Pengungsi Rohingya di Indonesia dengan Metode Support Vector Machine dan Naïve Bayes,” J. Media Inform. Budidarma, vol. 8, no. 2, p. 748, 2024, doi: 10.30865/mib.v8i2.7517.

A. Muhammadin and I. A. Sobari, “Analisis Sentimen Pada Ulasan Aplikasi Kredivo Dengan Algoritma Svm Dan Nbc,” Reputasi J. Rekayasa Perangkat Lunak, vol. 2, no. 2, pp. 85–91, 2021, doi: 10.31294/reputasi.v2i2.785.

G. K. Locarso, “Analisis Sentimen Review Aplikasi Pedulilindungi Pada Google Play Store Menggunakan Nbc,” JTIK (Jurnal Tek. Inform. Kaputama), vol. 6, no. 2, pp. 353–361, 2022, doi: 10.59697/jtik.v6i2.207.

S. Kurniawan, W. Gata, D. A. Puspitawati, M. Tabrani, and K. Novel, “Perbandingan Metode Klasifikasi Analisis Sentimen Tokoh Politik Pada Komentar Media Berita Online,” vol. 1, no. 10, pp. 2–8, 2021.

M. H. Saragih and A. S. Girsang, “Sentiment analysis of customer engagement on social media in transport online,” Proc. - 2017 Int. Conf. Sustain. Inf. Eng. Technol. SIET 2017, vol. 2018-Janua, pp. 24–29, 2017, doi: 10.1109/SIET.2017.8304103.

P. S. M. Suryani, L. Linawati, and K. O. Saputra, “Penggunaan Metode Naïve Bayes Classifier pada Analisis Sentimen Facebook Berbahasa Indonesia,” Maj. Ilm. Teknol. Elektro, vol. 18, no. 1, p. 145, 2019, doi: 10.24843/mite.2019.v18i01.p22.

B. agus Sholekha Inez, Faqih Ahmad, “Sentiment Analysis Of Publik Opinion Covid Vaccine Using Naive Bayes And Random Forest Methods,” J. Tek. Inform. Atmaluhur, vol. 6, no. 1, p. 4, 2022.

A. R. Isnain, A. I. Sakti, D. Alita, and N. S. Marga, “Sentimen Analisis Publik Terhadap Kebijakan Lockdown Pemerintah Jakarta Menggunakan Algoritma Svm,” J. Data Min. dan Sist. Inf., vol. 2, no. 1, p. 31, 2021, doi: 10.33365/jdmsi.v2i1.1021.

A. J. Rozaqi, A. Sunyoto, and M. rudyanto Arief, “Deteksi Penyakit Pada Daun Kentang Menggunakan Pengolahan Citra dengan Metode Convolutional Neural Network,” Creat. Inf. Technol. J., vol. 8, no. 1, p. 22, 2021, doi: 10.24076/citec.2021v8i1.263.

A. A. A. Suryati emi, Styawati Styawati, “Analisis Sentimen Transportasi Online Menggunakan Ekstraksi Fitur Model Word2vec Text Embedding Dan Algoritma Support Vector Machine,” J. Teknol. Dan Sist. Inf., vol. 4, no. 1, pp. 96–106, 2023.


Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Analisis Sentimen Tanggapan Publik di Twitter Terkait Program Kerja Makan Siang Gratis Prabowo–Gibran Menggunakan Algoritma Naïve Bayes Classifier dan Support Vector Machine

Dimensions Badge
Article History
Submitted: 2024-11-02
Published: 2024-12-03
Abstract View: 222 times
PDF Download: 256 times
How to Cite
Ramadhani, A., Permana, I., Afdal, M., & Fronita, M. (2024). Analisis Sentimen Tanggapan Publik di Twitter Terkait Program Kerja Makan Siang Gratis Prabowo–Gibran Menggunakan Algoritma Naïve Bayes Classifier dan Support Vector Machine. Building of Informatics, Technology and Science (BITS), 6(3), 1509−1516. https://doi.org/10.47065/bits.v6i3.6188
Issue
Section
Articles