Public Sentiment Analysis on the Boycott Israel Movement on Platform X Using Random Forest and Logistic Regression Algorithms
DOI:
https://doi.org/10.30871/jaic.v9i3.9551Keywords:
Boycott, Logistic Regression, Random Forest, Sentiment Analysis, Social MediaAbstract
This research aims to analyze public sentiment toward the boycott movement against Israel on the X platform by applying Random Forest and Logistic Regression algorithms. The study uses 616 tweets collected through web crawling with relevant keywords such as "Boikot", "Israel", and "Palestine", covering the period from March 1, 2023 to January 30, 2025. The dataset underwent preprocessing including cleaning, normalization, stopword removal, tokenization, and stemming. Sentiment labeling was conducted both manually, categorizing the data into positive, negative, and neutral classes. TF-IDF was used for feature weighting. The data was split into 80% training and 20% testing. The Random Forest model achieved an accuracy of 70%, while Logistic Regression reached 68%. Both models showed higher accuracy in predicting positive sentiment compared to negative and neutral. The results suggest that public opinion on the boycott movement on social media tends to be supportive, with “Boikot,” “Israel,” and “Palestine” being the most dominant terms. Random Forest performed slightly better in classification, though improvements are needed in recognizing non-positive sentiments.
Downloads
References
[1] J. Khatib Sulaiman, D. Atmajaya, A. Febrianti, H. Darwis, I. Artikel Abstrak, And K. Kunci, “Metode Svm Dan Naive Bayes Untuk Analisis Sentimen Chatgpt Di Twitter,” Indonesian Journal Of Computer Science Attribution, Vol. 12, No. 4, P. 2173, Doi: Https://Doi.Org/10.33022/Ijcs.V12i4.3341.
[2] Isalman, Ilyas, Farhan Ramadhani Istianandar, And Nurul Ittaqullah, “Boycott Campaign Intensity On Consumer Boycott Intentions And Participation: The Role Of Access To Substitute Products,” Journal Of Economics, Business, And Accountancy Ventura, Vol. 27, No. 3, Pp. 430–444, Mar. 2025, Doi: 10.14414/Jebav.V27i3.4737.
[3] S. Fatimah Azzahro, “Sentiment Analysis On Boycott Movement: Nvivo Approach,” 2024. [Online]. Available: Https://Ejournal.Stiesyariahbengkalis.Ac.Id/Index.Php/Iqtishaduna
[4] P. Wahyuni And M. A. Romli, “Comparison Of Naïve Bayes Classifier And Decision Tree Algorithms For Sentiment Analysis On The House Of Representatives’ Right Of Inquiry On Twitter,” 2024. Doi: Https://Doi.Org/10.30871/Jaic.V8i2.8670.
[5] Ade Tiara Susilawati, Nur Anjeni Lestari, And Puput Alpria Nina, “Analisis Sentimen Publik Pada Twitter Terhadap Boikot Produk Israel Menggunakan Metode Naïve Bayes,” Nian Tana Sikka : Jurnal Ilmiah Mahasiswa, Vol. 2, No. 1, Pp. 26–35, Dec. 2023, Doi: 10.59603/Niantanasikka.V2i1.240.
[6] N. Agung, P. #1, And H. Bunyamin, “Perbandingan Logistic Regression Dengan Random Forest Dalam Memprediksi Sentimen Pada Imdb Moview Review.”
[7] M. Yasir, M. Grace Haque, R. Suraji, And C. Author, “Analisis Sentimen Terhadap Kontroversi Fatwa Mui Nomor 83 Tahun 2023 Tentang Pemboikotan Produk Yang Terafiliasi Israel”, Doi: 10.31933/Jemsi.V5i4.
[8] M. Rusdi Rahman, A. Febri Diansyah, And H. Hanafi, “Sentiment Analysis On The Shopee Application On Playstore Using The Random Forest Classification Method,” Inform : Jurnal Ilmiah Bidang Teknologi Informasi Dan Komunikasi, Vol. 9, No. 1, Pp. 20–24, Nov. 2023, Doi: 10.25139/Inform.V9i1.5465.
[9] Alisya Mutia Mantika, Agung Triayudi, And Rima Tamara Aldisa, “Sentiment Analysis On Twitter Using Naïve Bayes And Logistic Regression For The 2024 Presidential Election,” Sana: Journal Of Blockchain, Nfts And Metaverse Technology, Vol. 2, No. 1, Pp. 44–55, Feb. 2024, Doi: 10.58905/Sana.V2i1.267.
[10] F. Mulya, S. Putra, S. Rakasiwi, And N. Ariyanto, “Twitter Sentiment Classification Towards Telecommunication Provider Users In Indonesia,” 2025. Doi: Https://Doi.Org/10.30871/Jaic.V9i2.9143.
[11] I. P. Rahayu, A. Fauzi, And J. Indra, “Analisis Sentimen Terhadap Program Kampus Merdeka Menggunakan Naive Bayes Dan Support Vector Machine,” Jurnal Sistem Komputer Dan Informatika (Json), Vol. 4, No. 2, P. 296, Dec. 2022, Doi: 10.30865/Json.V4i2.5381.
[12] B. Ramadhani And R. R. Suryono, “Komparasi Algoritma Naïve Bayes Dan Logistic Regression Untuk Analisis Sentimen Metaverse,” Jurnal Media Informatika Budidarma, Vol. 8, No. 2, P. 714, Apr. 2024, Doi: 10.30865/Mib.V8i2.7458.
[13] I. Habib Kusuma And N. Cahyono, “Analisis Sentimen Masyarakat Terhadap Penggunaan E-Commerce Menggunakan Algoritma K-Nearest Neighbor,” Vol. 8, No. 3, 2023, Doi: Https://Doi.Org/10.30591/Jpit.V8i3.5734.
[14] M. R. Nurhusen, J. Indra, And K. A. Baihaqi, “Analisis Sentimen Pengguna Twitter Terhadap Kenaikan Harga Bahan Bakar Minyak (Bbm) Menggunakan Metode Logistic Regression,” Jurnal Media Informatika Budidarma, Vol. 7, No. 1, P. 276, Jan. 2023, Doi: 10.30865/Mib.V7i1.5491.
[15] R. A. Ramadhan, T. Rohana, T. Al Mudzakir, And D. Wahiddin, “Penerapan Algoritma Support Vector Machines Dan Random Forest Dalam Analisis Sentimen Ulasan Aplikasi Identitas Kependudukan Digital,” Jurnal Teknik Informasi Dan Komputer (Tekinkom), Vol. 7, No. 2, P. 969, Dec. 2024, Doi: 10.37600/Tekinkom.V7i2.1595.
[16] E. R. Lidinillah, T. Rohana, And A. R. Juwita, “Analisis Sentimen Twitter Terhadap Steam Menggunakan Algoritma Logistic Regression Dan Support Vector Machine,” Teknosains : Jurnal Sains, Teknologi Dan Informatika, Vol. 10, No. 2, Pp. 154–164, Jul. 2023, Doi: 10.37373/Tekno.V10i2.440.
[17] A. M. Siregar, “Analisis Sentimen Pindah Ibu Kota Negara (Ikn) Baru Pada Twitter Menggunakan Algoritma Naive Bayes Dan Support Vector Machine (Svm),” Faktor Exacta, Vol. 16, No. 3, Oct. 2023, Doi: 10.30998/Faktorexacta.V16i3.16703.
[18] A. Rizky Gunawan, R. Faticha, And A. Aziza, “Sentiment Analysis Using Lstm Algorithm Regarding Grab Application Services In Indonesia,” 2025. Doi: Https://Doi.Org/10.30871/Jaic.V9i2.8696.
[19] N. Salwa Amarta And I. Susila, “Analysis Of The Influence Of Religious Beliefs And Solidarity On Product Boycott Behavior,” 2025.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Rachmayanti Tri Agustin, Yana Cahyana, Kiki Ahmad Baihaqi, Tatang Rohana
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) ) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
