SENTIMENT CLASSIFICATION OF PUBLIC PERCEPTIONS ON RP200 TRILLION HIMBARA STIMULUS USING NAÏVE BAYES
DOI:
https://doi.org/10.34288/jri.v8i2.500Keywords:
Sentiment Analysis, Naïve Bayes Classifier, TF-IDF, government policy, instagramAbstract
The government's policy in the form of a fund stimulus of Rp200 trillion to the Himpunan Bank Milik Negara (HIMBARA) is a strategic step to maintain national economic stability and encourage real sector recovery. However, the implementation of public policy is inseparable from the response and public perception that develops on social media. This study aims to classify public sentiment towards the Rp200 trillion fund stimulus policy to Bank HIMBARA based on Instagram user comments and test the performance of the Naïve Bayes Classifier method in analyzing public policy sentiment. This study uses a quantitative approach with text mining and machine learning methods. Data in the form of 1.309 Instagram comments was collected through web scraping techniques from several online media accounts, then processed through text preprocessing and manual labeling stages into positive, neutral, and negative sentiments. Feature weighting was carried out using TF-IDF, then the data were classified using Multinomial Naïve Bayes and Complement Naïve Bayes. The results show that the Complement Naïve Bayes model achieved the best performance with an accuracy of 84%, an F1-score of 81%, and a high ROC-AUC value. These findings indicate that the majority of public sentiment toward the stimulus policy tends to be positive, and that the Naïve Bayes method is effective for social media–based sentiment analysis.
Downloads
References
Alfaridzy, M. A., Haerani, E., Jasril, & Oktavia, L. (2025). Klasifikasi Sentimen Masyarakat Terhadap Efisiensi Anggaran Pemerintah Menggunakan Metode Naïve Bayes Classifier. JUTECH: Journal Education and Technology,6(1), 169–182.
Alfawas, T. I., Rahim, A., & Rudiman, R. (2024). Penerapan Fitur Ekstraksi TF-IDF untuk Analisis Sentimen Ulasan Game Bus Simulator Indonesia dengan Algoritma Naive Bayes. Innovative: Journal Of Social Science Research, 4(5), 3177–3193. https://doi.org/0.31004/innovative.v4i5.13975
Alshehri, A., & Algarni, A. (2023). TF-TDA: A Novel Supervised Term Weighting Scheme for Sentiment Analysis. Electronics (Switzerland), 12(7). https://doi.org/10.3390/electronics12071632
Azzahra, W. L., & Mailoa, E. (2025). Analisis Sentimen terhadap RSUD Salatiga Menggunakan SVM dan TF-IDF. Jurnal Indonesia : Manajemen Informatika Dan Komunikasi, 6(1), 478–489. https://doi.org/10.35870/jimik.v6i1.1208
Balan Pratama, D., Sofwan, A., & Syafei, W. A. (2021). Implementasi Teknik Web Scraping Dan Fitur Data Eksternal Pada Sistem Informasi Dosen Penelitian Dan Pengabdian Dosen Fakultas Teknik Universitas Diponegoro. Transient: Jurnal Ilmiah Teknik Elektro, 10(2), 292–299. https://doi.org/10.14710/transient.v10i2.292-299
Hidayat, R. (2024). Penerapan Naïve Bayes Classifier Dalam Klasifikasi Sentimen Publik Di Twitter Terhadap Puan Maharani [UIN Sultan Syarif Kasim Riau]. https://repository.uin-suska.ac.id/80539/1/RIZKI HIDAYAT.pdf
Konstantakopoulou, I. (2023). Financial Intermediation, Economic Growth, and Business Cycles. Journal of Risk and Financial Management, 16(12), 1–9. https://doi.org/10.3390/jrfm16120514
Kusuma, A. T. A. (2024). Perbandingan Metode Peter Norvig, Lstm, Dan Ngram Untuk Spell Correction Bahasa Indonesia. Universitas Islam Indonesia.
Masrufah, L. (2022). Kebijakan Moneter dan Fiskal dalam Perekonomian. KASBANA : Jurnal Hukum Ekonomi Syariah, 2(1), 38–55. https://doi.org/10.53948/kasbana.v2i1.37
MasrufaUtama, P. (2022). Analisis Respons Publik di Media Sosial Terhadap Proses Legislasi RUU TNI Dalam Kerangka Demokrasi Deliberatif. Communicology Jurnal Ilmu Komunikasi, 13(1). https://doi.org/https://doi.org/10.21009/COMM.034.09
Mola, S. A. S., Djawa, S. N. R., & Mauko, A. Y. (2025). Text Mining Analisis Sentimen dengan Naïve Bayes (S. A. S. Mola (ed.)). Kaizeng Media ublishing.
Naraswati, N. P. G., Rosmilda, D. C., Desinta, D., Khairi, F., Damaiyanti, R., & Nooraeni, R. (2021). Analisis Sentimen Publik dari Twitter Tentang Kebijakan Penanganan Covid-19 di Indonesia dengan Naive Bayes Classification. SISTEMASI: Jurnal Sistem Informasi, 10(1), 222–238.
Nugraha, S. A., & Siregar, M. U. (2021). Application of The Naive Bayes Classifier Method In The Sentiment Analysis of Twitter User About The Capital City Relocation Running Title: Application of The Naive Bayes Classifier Method. PROC. INTERNAT. CONF. SCI. ENGIN, 4, 171–175.
Nurainun, N., Haerani, E., Syafria, F., & Oktavia, L. (2023). Penerapan Algoritma Naïve Bayes Classifier Dalam Klasifikasi Status Gizi Balita dengan Pengujian K-Fold Cross Validation. Journal of Computer System and Informatics (JoSYC), 4(3), 578–586. https://doi.org/10.47065/josyc.v4i3.3414
Nurrochmah, D. S., Rahaningsih, N., Dana, R. D., & Rohmat, C. L. (2025). Penerapan Algoritma Naive Bayes Dalam Analisis SentimenUlasan Aplikasi Kitalulus di Google Play Store. Jurnal Informatika Terpadu, 6(1), 29–37. https://doi.org/10.54914/jit.v11i1.1544
Nurwanda, Suarna, N., & Prihartono, W. (2024). Penerapan NLP (Natural Language Processing) Dalam Analisis Sentimen Pengguna Telegram di PlayStore. Jurnal Mahasiswa Teknik Informatika, 8(2), 1841–1846. https://doi.org/10.36040/jati.v8i2.8469
Prayugah, M. I., Indahyanti, U., & Ariyanti, N. (2024). Analisis Sentimen Publik atas Respons Pemerintah pada Serangan Ransomware dengan Pendekatan Machine Learning dan Smote. JOISIE (Journal Of Information Systems And Informatics Engineering), 8(2), 333. https://doi.org/10.35145/joisie.v8i2.4764
Putra, I. G. S. D., & Putra, I. N. T. A. (2025). Implementasi Metode Naïve Bayes Pada Analisis Sentimen Pengguna Aplikasi Mobile Kita Bisa. Jurnal Informatika dan Teknik Elektro Terapan, 13(2), 1202–1211. https://doi.org/10.23960/jitet.v13i2.6423
Rahayu, D. W. Y., Umam, K., & Handayani, M. R. (2025). Performance of Machine Learning Algorithms on Imbalanced Sentiment Datasets Without Balancing Techniques. Journal of Applied Informatics and Computing, 9(3), 998–1005. https://doi.org/10.30871/jaic.v9i3.9584
RI, K. (2025). Strategi Fiskal Indonesia 2025–2026: Menjaga Stabilitas Ekonomi di Tengah Ketidakpastian Global. Kementrian Keuangan RI Direktoral Jendral Pembendaharaan. https://djpb.kemenkeu.go.id/kppn/saumlaki/id/data-publikasi/berita-terbaru/2987-strategi-fiskal-indonesia-2025–2026-menjaga-stabilitas-ekonomi-di-tengah-ketidakpastian-global.html
Rieuwpassa, J. A., Sugito, & Widiharih, T. (2024). Implementasi Metode Naive Bayes Classifier Untuk Klasifikasi Sentimen Ulasan Pengguna Aplikasi Netflix Pada Google Play. Jurnal Gaussian, 12(3), 362–371. https://doi.org/10.14710/j.gauss.12.3.362-371
Saputri, B. (2023). Pengaruh Loan To Deposit Ratio dan Return On Asset Terhadap Harga Saham (Himpunan Bank Milik Negera) Yang Terdaftar di Bursa Efek Indonesia Periode 2018-2022 [Universitas Batanghari Jambi]. http://repository.unbari.ac.id/2697/1/Besse Saputri.pdf
Setiawan, F. A. A., & Fathonah, R. N. S. (2025). Tinjauan Sistematis Literatur : Analisis Sentimen Terhadap. JATI : Jurnal Mahasiswa Teknik Informatika, 9(5), 7995–8003. https://doi.org/10.36040/jati.v9i5.14963
Silitonga, R. N., & Manda, G. S. (2022). Pengaruh Risiko Kredit dan Risiko Likuiditas terhadap Kinerja Keuangan pada Bank BUMN Periode 2015-2020. Jurnal Maksipreneur: Manajemen, Koperasi, dan Entrepreneurship, 12(1), 22. https://doi.org/10.30588/jmp.v12i1.948
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Wan Sobri Amin, Muhammad Fikry, Rahmad Abdillah, Surya Agustian
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The Jurnal Riset Informatika has legal rules for accessing digital electronic articles uunder a Creative Commons Attribution-NonCommercial 4.0 International License . Articles published in Jurnal Riset Informatika, provide Open Access, for the purpose of scientific development, research, and libraries.
