Comparative Study of Naïve Bayes Classifier and Support Vector Machine Methods in Public Sentiment Analysis of Prabowo-Gibran's Free Lunch Program
DOI:
https://doi.org/10.70609/g-tech.v9i3.7248Keywords:
Sentiment Analysis, Naïve Bayes classifier, Support Vector Machine, Social Media, Free Lunch ProgramAbstract
In today's digital era, social media has become the main platform for people to voice their opinions on social and political issues. One of the most discussed topics is the free lunch program of President-elect Prabowo Subianto and Vice President-elect Gibran Rakabuming Raka. The program triggered various public reactions, making it relevant for sentiment analysis. The purpose of this study is to compare the performance of two text classification algorithms-Naïve Bayes and Support Vector Machine (SVM)-in classifying public sentiment towards the program. The dataset was obtained from Kaggle, with 657 initial data. After preprocessing, 156 data remained, consisting of 127 negative sentiments and 31 positive sentiments. Data processing followed the CRISP-DM framework, with Python and Scikit-learn used in model training. The results showed that the naive bayes classifier performed better with 84.38% accuracy, 86.90% precision, and 84.38% recall. Support Vector Machine showed lower performance in all metrics. In addition, the Naive Bayes Classifier was able to classify sentiments in a more balanced manner. The analysis was performed using Jupyter Notebook, and the final model was implemented through a Streamlit-based web interface.
References
Ayu Syafarina, G., & Kalimantan Muhammad Arsyad Al Banjari Banjarmasin, I. (n.d.). Implementasi Framework Streamlit Sebagai Prediksi Harga Jual Rumah Dengan Linear Regresi. 7, 2023. https://doi.org/10.47002/metik.v7i2.680
Desiani, N., & Syafiq, A. (2025). Efektivitas Program Makan Gratis pada Status Gizi Siswa Sekolah Dasar: Tinjauan Sistematis. Malahayati Nursing Journal, 7(1), 27–48. https://doi.org/10.33024/mnj.v7i1.17497
Felicia Watratan, A., Puspita, A. B., Moeis, D., Informasi, S., & Profesional Makassar, S. (2020). Implementasi Algoritma Naive Bayes Untuk Memprediksi Tingkat Penyebaran Covid-19 Di Indonesia. In JOURNAL OF APPLIED COMPUTER SCIENCE AND TECHNOLOGY (JACOST) (Vol. 1, Issue 1). http://journal.isas.or.id/index.php/JACOST
Gaffar, A. W. M., Halis, A. M., Purnawansyah, P., & Jabir, S. R. (2024). Penerapan Algoritma Support Vector Machine untuk Klasifikasi Stunting pada Balita di Kabupaten Enrekang. Jurnal Minfo Polgan, 13(1), 286–292. https://doi.org/10.33395/jmp.v13i1.13620
Kinerja Naïve Bayes, P., Supian, A., Tri Revaldo, B., Marhadi, N., & Efrizoni, L. (n.d.). Acuan Supian Perbandingan Kinerja Naïve Bayes dan SVM pada Analisis Sentimen Twitter Ibukota Nusantara. https://github.com/syenirasheila/Sentiment-Analysis-IKN-
lukman,+609-Analisis+Sentimen+Ulasan+Aplikasi+MyPertamina+pada+Google+Play+Store+menggunakan+Algoritma+NBC. (n.d.).
Miftahusalam, A., Febby Nuraini, A., Khoirunisa, A. A., & Pratiwi, H. (n.d.). Perbandingan Algoritma Random Forest, Naïve Bayes, dan Support Vector Machine Pada Analisis Sentimen Twitter Mengenai Opini Masyarakat Terhadap Penghapusan Tenaga Honorer.
Rosyida, T., Putro, H. P., & Wahyono, H. (n.d.). ANALISIS SENTIMEN TERHADAP PILPRES 2024 BERDASARKAN OPINI DARI TWITTER MENGGUNAKAN NAÏVE BAYES DAN SVM. www.apjii.or.id
Singgalen, Y. A. (2023). Analisis Sentimen Pengunjung Pulau Komodo dan Pulau Rinca di Website Tripadvisor Berbasis CRISP-DM. Journal of Information System Research (JOSH), 4(2), 614–625. https://doi.org/10.47065/josh.v4i2.2999
Suryati, E., Ari Aldino, A., Penulis Korespondensi, N., & Suryati Submitted, E. (2023). Analisis Sentimen Transportasi Online Menggunakan Ekstraksi Fitur Model Word2vec Text Embedding Dan Algoritma Support Vector Machine (SVM). 4(1), 96–106. https://doi.org/10.33365/jtsi.v4i1.2445
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Fikri Febrian, Zaehol Fatah , Achmad Baijuri
This work is licensed under a Creative Commons Attribution 4.0 International License.
