Model Machine Learning Untuk Analisis Sentimen Masyarakat Terhadap Kenaikan PPN di Media Sosial X
DOI:
https://doi.org/10.47065/bulletincsr.v5i4.523Keywords:
PPN 12%; Sentiment Analysis; Machine Learning; Social Media X; SVM; Random Forest; Decision TreeAbstract
This study examines people's reactions to the Indonesian government's plan to adjust the VAT rate from 11% to 12%, which is scheduled to take effect in 2025. This policy triggered a variety of opinions among netizens, especially on the social networking service X. To explore public opinion, data was collected through web crawling techniques from October to December 2024, resulting in 1,871 records. Then the dataset was preprocessed by text cleaning, case folding, tokenization, stopword removal, and stemming, and the dataset was reduced to 1806. In addition, up to 1000 data will be manually labeled, negative, neutral, positive, by language experts to ensure that each sentence has the appropriate label. These data are used for testing and training, then up to 806 unlabeled data are used as final testing. At the word weighting stage, the Term Frequency-Inverse Document Frequency (TF-IDF) method is used to perform the process. In this study, three machine learning algorithms were used to compare the classification performance, namely Support Vector Machine (SVM), Random Forest, and Decision Tree. Based on the evaluation results, the SVM algorithm recorded the highest accuracy rate of 94%, followed by Random Forest with 93% and Decision Tree with 91%. The results showed a predominance of negative sentiments, indicating public dissatisfaction with the policy. This study proves that machine learning techniques can be effectively used to capture public perceptions through social media, which in turn can be a benchmark for the government to make decisions that will be enforced.
Downloads
References
N. Fauziah, “Analisis Sentimen Publik Terhadap Kenaikan Tarif PPN di Indonesia dengan Pendekatan VADER,” Jurnal Akuntansi dan Keuangan, vol. 12, no. 2, p. 228, Sep. 2024, doi: 10.29103/jak.v12i2.16796.
I. M. Putri, “Kenaikan Ppn 12% Dan Dampaknya Terhadap Eknomi,” Jurnal Ilmiah Manajemen, Ekonomi, & Akuntansi (MEA), vol. 8, no. 2, pp. 934–944, 2024, doi: https://doi.org/10.31955/mea.v8i2.4077.
S. Styawati and F. Ariany, “Sistem Monitoring Tumbuh Kembang Balita/Batita di Tengah Covid-19 Berbasis Mobile,” Jurnal Informatika Universitas Pamulang, vol. 5, no. 4, p. 490, Dec. 2021, doi: 10.32493/informatika.v5i4.7067.
L. Nursinggah, T. Mufizar, and U. Perjuangan, “Analisis Sentimen Pengguna Aplikasi X Terhadap Program Makan Siang Gratis Dengan Metode Naïve Bayes Classifier,” J. Inform. dan Tek. Elektro Ter, vol. 12, no. 3, 2024, doi: http://dx.doi.org/10.23960/jitet.v12i3.4336.
J. Siagian and P. Painem, “Analisis Sentimen Masyarakat Indonesia Terhadap Rencana Kenaikan Ppn Menjadi 12% Di Media Sosial Twiter/X Menggunakan Metode Naïve Bayes,” in Prosiding Seminar Nasional Mahasiswa Fakultas Teknologi Informasi (SENAFTI), 2024, pp. 779–786. Accessed: May 04, 2025. [Online]. Available: https://senafti.budiluhur.ac.id/senafti/article/view/1499
D. Oktavia, Y. R. Ramadahan, and M. Minarto, “Analisis Sentimen Terhadap Penerapan Sistem E-Tilang Pada Media Sosial Twitter Menggunakan Algoritma Support Vector Machine (SVM),” KLIK: Kajian Ilmiah Informatika dan Komputer, vol. 4, no. 1, pp. 407–417, 2023, Accessed: May 04, 2025. [Online]. Available: https://djournals.com/klik/article/view/1040
Berliana Nur Isnayni, Nurirwan Saputra, and Tri Hastono, “SENTIMENT ANALYSIS OF COFFEE SHOP REVIEWS USING RANDOM FOREST CLASSIFIER METHOD,” JTH: Journal of Technology and Health, vol. 1, no. 4, pp. 233–244, May 2024, doi: 10.61677/jth.v2i2.152.
Wulandari Wulandari, Nofiyani Nofiyani, and Yesi Puspita Dewi, “Analisis Sentimen terhadap Ulasan Aplikasi Canva di Play Store dengan Menggunakan Pendekatan Lexicon dan Algoritma Decision Tree,” Jurnal Ticom: Technology of Information and Communication, vol. 13, no. 2, pp. 57–63, Jan. 2025, doi: 10.70309/ticom.v13i2.133.
T. Muhayat, A. Fauzi, and J. Indra, “Analisis Sentimen Terhadap Komentar Video Youtube Menggunakan Support Vector Machines,” Progresif: Jurnal Ilmiah Komputer, vol. 19, no. 1, p. 231, Feb. 2023, doi: 10.35889/progresif.v19i1.1060.
N. A. R. Putri, “Analisis Jaringan pada Media Sosial X dengan# Boikot Menggunakan Social Network Analysis,” IJITECH: Indonesian Journal of Information Technology, vol. 2, no. 1, pp. 11–15, 2024, Accessed: May 04, 2025. [Online]. Available: https://ojisnu.isnuponorogo.org/index.php/ijitech/article/view/79
Rahmania Mustaqlillah, Okky Widyaningtyas, and Tri Wantoro, “Efektivitas Penggunaan Twitter Sebagai Sarana Peningkatan Berpikir Kritis Mahasiswa Ilmu Komunikasi,” MUKASI: Jurnal Ilmu Komunikasi, vol. 2, no. 1, pp. 18–28, Feb. 2023, doi: 10.54259/mukasi.v2i1.1346.
M. R. Pratama, A. Fauzi, D. Wahiddin, and A. R. Pratama, “Analisis Sentimen Kebijakan Pembelian Gas 3 Kg dengan KTP Menggunakan Naïve Bayes,” Jutisi?: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi, vol. 13, no. 2, p. 1338, Aug. 2024, doi: 10.35889/jutisi.v13i2.2168.
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.
F. N. Azzahra, T. Rohana, R. Rahmat, and A. R. Juwita, “Penerapan Metode Naive Bayes Dalam Klasifikasi Spam SMS Menggunakan Fitur Teks Untuk Mengatasi Ancaman Pada Pengguna,” Journal of Information System Research (JOSH), vol. 5, no. 3, pp. 873–880, 2024, Accessed: May 04, 2025. [Online]. Available: https://ejurnal.seminar-id.com/index.php/josh/article/view/5070
N. Romadoni, A. Mutoi Siregar, D. S. Kusumaningrum, and T. Rohana, “Classification Model of Public Sentiments About Electric Cars Using Machine Learning,” Scientific Journal of Informatics, vol. 11, no. 2, 2024, doi: 10.15294/sji.v11i2.1309.
A. D. Sidik and A. Ansawarman, “Prediksi Jumlah Kendaraan Bermotor Menggunakan Machine Learning,” Formosa Journal of Multidisciplinary Research, vol. 1, no. 3, pp. 559–568, Jul. 2022, doi: 10.55927/fjmr.v1i3.745.
Y. A. Singgalen, “Analisis Sentimen Wisatawan terhadap Taman Nasional Bunaken dan Top 10 Hotel Rekomendasi Tripadvisor Menggunakan Algoritma SVM dan DT berbasis CRISP-DM,” Journal of Computer System and Informatics (JoSYC), vol. 4, no. 2, pp. 367–379, Feb. 2023, doi: 10.47065/josyc.v4i2.3092.
A. M. Pravina, I. Cholisoddin, and P. P. Adikara, “Analisis sentimen tentang opini maskapai penerbangan pada dokumen twitter menggunakan algoritme support vector machine (svm),” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 3, no. 3, pp. 2789–2797, 2019, [Online]. Available: https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/4793
Ihsan Zulfahmi, “Analisis Sentimen Aplikasi PLN Mobile Menggunakan Metode Decission Tree,” Jurnal Penelitian Rumpun Ilmu Teknik, vol. 3, no. 1, pp. 11–21, Dec. 2023, doi: 10.55606/juprit.v3i1.3096.
N. Arib Fadhlurrohman, A. Primajaya, and A. Nugraha Dimyati, “Analisis Sentimen Terhadap Skema Student Loan Untuk Biaya Perguruan Tinggi Pada Twitter Menggunakan Algoritma Naïve Bayes,” JATI (Jurnal Mahasiswa Teknik Informatika), vol. 9, no. 2, pp. 2115–2123, Mar. 2025, doi: 10.36040/jati.v9i2.13001.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Model Machine Learning Untuk Analisis Sentimen Masyarakat Terhadap Kenaikan PPN di Media Sosial X
ARTICLE HISTORY
How to Cite
Issue
Section
Copyright (c) 2025 Ilham Ridho Pratama, Yana Cahyana, Rahmat, Deden Wahiddin
This work is licensed under a Creative Commons Attribution 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 Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment 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 acknowledgment 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 (Refer to The Effect of Open Access).
