Analysis of Public Sentiment Indonesia’s Personal Data Protection Law: A Comparison of SVM and IndoBERT on X Platform
DOI:
https://doi.org/10.52436/1.jutif.2026.7.2.5415Keywords:
IndoBERT, Privacy Data Protection, Public Sentiment, Sentiment Analysis, Support Vector Machine (SVM), UU PDPAbstract
The high number of data misuses, thefts, and leaks led to the enactment of the PDP Law, which regulates the rights and obligations of data owners and electronic system providers. The purpose of this study is to examine the public’s response to the implementation of the law through the X platform, using tweet harvest as a scraping tool, and to evaluate model performance through a comparative approach between SVM and BERT. The feature extraction used in this study is TF-IDF for SVM and BERT with IndoBERT. The accuracy results indicate that BERT is better with an accuracy of 86% compared to SVM with a training and test data ratio of 85:15. This advantage is because BERT can understand linguistic context that SVM cannot. On the other hand, SVM has advantages in computational efficiency and faster processing, making it a suitable choice in situations with limited computational resources.
The sentiment analysis result revealed that data protection, digital footprint and the institution's role were the most frequently discussed topics. Furthermore, periodic or real-time evaluations can be conducted on the public's response to the PDP Law to ensure it remains aligned and relevant to technological developments and societal needs.
Downloads
References
M. Amin et al., “Security and privacy awareness of smartphone users in Indonesia,” in Journal of Physics: Conference Series, IOP Publishing Ltd, May 2021. doi: 10.1088/1742-6596/1882/1/012134.
I. G. N. P. Widiatedja and N. Mishra, “Establishing an independent data protection authority in Indonesia: a future–forward perspective,” International Review of Law, Computers and Technology, vol. 37, no. 3, pp. 252–273, 2023, doi: 10.1080/13600869.2022.2155793.
Republik Indonesia, Undang-Undang (UU) Nomor 27 Tahun 2022 tentang Pelindungan Data Pribadi. Indonesia: Kementerian Komunikasi dan Digital RI (JDHI Komdigi), 2022. Accessed: Dec. 08, 2025. [Online]. Available: https://jdih.komdigi.go.id/produk_hukum/view/id/832/t/undangundang+nomor+27+tahun+2022
CNBC Indonesia, “Lembaga Perlindungan Data Pribadi Belum Dibentuk, Ini Kata Angga Raka.” Accessed: Nov. 03, 2024. [Online]. Available: https://www.cnbcindonesia.com/tech/20241021174853-37-581818/lembaga-perlindungan-data-pribadi-belum-dibentuk-ini-kata-angga-raka
H. Al Jannah and D. Hermawan, “Analysis of Indonesian Society’s Perceptions of the COVID-19 Vaccine in Youtube Comments Using Machine Learning Algorithms,” in 2022 3rd International Conference on Artificial Intelligence and Data Sciences: Championing Innovations in Artificial Intelligence and Data Sciences for Sustainable Future, AiDAS 2022 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2022, pp. 72–77. doi: 10.1109/AiDAS56890.2022.9918796.
M. N. Fakhruzzaman, S. Z. Jannah, S. W. Gunawan, A. I. Pratama, and D. A. Ardanty, “IndoPolicyStats: sentiment analyzer for public policy issues,” Bulletin of Electrical Engineering and Informatics, vol. 13, no. 1, pp. 482–489, Feb. 2024, doi: 10.11591/eei.v13i1.5263.
Z. D. W. Putra, “The Sentiments of Indonesian Urban Citizens Regarding the Lockdown-Like Policy During the COVID-19 Pandemic: A Path Towards an Urban E-Planning Process in a Pandemic Situation INTRODUCTION,” International Journal of E-Planning Research, vol. 11, no. 1, 2022, doi: 10.4018/IJEPR.297515.
E. A. Sukma, A. N. Hidayanto, A. I. Pandesenda, A. N. Yahya, P. Widharto, and U. Rahardja, “Sentiment Analysis of the New Indonesian Government Policy (Omnibus Law) on Social Media Twitter,” in Proceedings - 2nd International Conference on Informatics, Multimedia, Cyber, and Information System, ICIMCIS 2020, Institute of Electrical and Electronics Engineers Inc., Nov. 2020, pp. 153–158. doi: 10.1109/ICIMCIS51567.2020.9354287.
P. Maulana, I. Budi, and A. Budi Santoso, “Sentiment Analysis of Indonesian Government’s Effort to Overcome the Unemployment Problem during COVID-19 Pandemic,” in ICOIACT 2022 - 5th International Conference on Information and Communications Technology: A New Way to Make AI Useful for Everyone in the New Normal Era, Proceeding, Institute of Electrical and Electronics Engineers Inc., 2022, pp. 144–149. doi: 10.1109/ICOIACT55506.2022.9971853.
D. S. A. Maylawati, M. T. S. Bilhaq, A. Wahana, D. R. Ramdania, E. Nurlatifah, and M. A. Ramdhani, “Analysis of Changes in Sentiment towards COVID-19 Vaccination in Indonesia Using the Convolutional Neural Network,” in 2023 11th International Conference on Cyber and IT Service Management, CITSM 2023, Institute of Electrical and Electronics Engineers Inc., 2023. doi: 10.1109/CITSM60085.2023.10455282.
M. Pilliang, H. Akbar, and G. Firmansyah, “Sentiment Analysis for Super Applications in Indonesia: A Case Study of Gov2Go App,” in Proceedings of the International Conference on Electrical Engineering and Informatics, Institute of Electrical and Electronics Engineers Inc., 2022, pp. 80–85. doi: 10.1109/IConEEI55709.2022.9972291.
M. A. Khadija, I. S. D. Jayanti, and F. U. Nimah, “Towards Smart City: Aspect Based Sentiment Analysis of Indonesian Public Aspiration Complaints Data Using Machine Learning,” in Proceedings - International Conference on Informatics and Computational Sciences, Institute of Electrical and Electronics Engineers Inc., 2024, pp. 215–220. doi: 10.1109/ICICoS62600.2024.10636859.
D. Gunawan, S. N. Awwal, and F. N. H. Afif, “Sentiment Analysis of Personal Data Protection and Privacy Law in Indonesia Using Multinomial Naive Bayes Algorithm,” in 2025 International Conference on Smart Computing, IoT and Machine Learning, SIML 2025, Institute of Electrical and Electronics Engineers Inc., 2025. doi: 10.1109/SIML65326.2025.11080902.
D. Dennis et al., “Sentiment Classification Against the Public Activity Restrictions Policy in Jakarta Using Machine Learning Models,” Institute of Electrical and Electronics Engineers (IEEE), Feb. 2022, pp. 5–9. doi: 10.1109/icaibda53487.2021.9689741.
D. Sugiarto, E. Utami, A. Yaqin, and S. Raharjo, “Sentiment Analysis of Cooking Oil Price Policy in Indonesia using Word2Vec Feature Extraction and Bidirectional Long Short Term Memory,” in 2023 10th International Conference on Advanced Informatics: Concept, Theory and Application, ICAICTA 2023, Institute of Electrical and Electronics Engineers Inc., 2023. doi: 10.1109/ICAICTA59291.2023.10389977.
L. Sandra and S. Aritonang, “Lockdown Countdown: Lockdown Sentiment Analysis on Twitter Using Artificial Neural Network,” in 2021 International Conference on Data Science and Its Applications, ICoDSA 2021, Institute of Electrical and Electronics Engineers Inc., 2021, pp. 198–202. doi: 10.1109/ICoDSA53588.2021.9617212.
L. Alfat, N. Uddin, M. Nasucha, M. Mohamad, and S. Masrom, “Analysis of Indonesian Opinion on the Arrival of COVID-19 Vaccine Using Machine Learning,” in Proceedings - 11th International Conference on Information Technology, Computer and Electrical Engineering, ICITACEE 2024, Institute of Electrical and Electronics Engineers Inc., 2024, pp. 326–331. doi: 10.1109/ICITACEE62763.2024.10761958.
N. Hasanati, T. S. Utami, and R. H. Kusumaningtyas, “Sentiment Analysis on News Headlines of Nation’s Capital Relocation Using CNN and SVM,” in 2023 11th International Conference on Cyber and IT Service Management, CITSM 2023, Institute of Electrical and Electronics Engineers Inc., 2023. doi: 10.1109/CITSM60085.2023.10455228.
S. F. Pane, J. Ramdan, A. G. Putrada, M. N. Fauzan, R. M. Awangga, and N. Alamsyah, “A Hybrid CNN-LSTM Model with Word-Emoji Embedding for Improving the Twitter Sentiment Analysis on Indonesia’s PPKM Policy,” in Proceeding - 6th International Conference on Information Technology, Information Systems and Electrical Engineering: Applying Data Sciences and Artificial Intelligence Technologies for Environmental Sustainability, ICITISEE 2022, Institute of Electrical and Electronics Engineers Inc., 2022, pp. 51–56. doi: 10.1109/ICITISEE57756.2022.10057720.
B. Waspodo, Q. Aini, F. R. Singgih, R. H. Kusumaningtyas, and E. Fetrina, “Support Vector Machine and Lexicon based Sentiment Analysis on Kartu Prakerja (Indonesia Pre-Employment Cards Government Initiatives),” in 2022 10th International Conference on Cyber and IT Service Management, CITSM 2022, Institute of Electrical and Electronics Engineers Inc., 2022. doi: 10.1109/CITSM56380.2022.9935990.
R. L. V. Nyoto and Y. Ruldeviyani, “Infiltration Wells Program in Jakarta: Twitter Sentiment Analysis,” in 2022 1st International Conference on Information System and Information Technology, ICISIT 2022, Institute of Electrical and Electronics Engineers Inc., 2022, pp. 352–357. doi: 10.1109/ICISIT54091.2022.9872911.
P. S. Saragih, D. Witarsyah, F. Hamami, and J. M. MacHado, “Sentiment Analysis of Social Media Twitter with Case of Large Scale Social Restriction in Jakarta using Support Vector Machine Algorithm,” in 2021 International Conference Advancement in Data Science, E-Learning and Information Systems, ICADEIS 2021, Institute of Electrical and Electronics Engineers Inc., 2021. doi: 10.1109/ICADEIS52521.2021.9701961.
I. Firmansyah, M. H. Asnawi, S. A. Hasanah, R. Novian, and A. A. Pravitasari, “A Comparison of Support Vector Machine and Naïve Bayes Classifier in Binary Sentiment Reviews for PeduliLindungi Application,” in 2021 International Conference on Artificial Intelligence and Big Data Analytics, ICAIBDA 2021, Institute of Electrical and Electronics Engineers Inc., 2021, pp. 140–145. doi: 10.1109/ICAIBDA53487.2021.9689771.
G. Kanugrahan and A. F. Wicaksono, “Sentiment Analysis of Face-to-face Learning during Covid-19 Pandemic using Twitter Data,” in Proceedings - 2021 8th International Conference on Advanced Informatics: Concepts, Theory, and Application, ICAICTA 2021, Institute of Electrical and Electronics Engineers Inc., 2021. doi: 10.1109/ICAICTA53211.2021.9640282.
M. Rahardi, A. Aminuddin, F. F. Abdulloh, and R. A. Nugroho, “Sentiment Analysis of Covid-19 Vaccination using Support Vector Machine in Indonesia,” 2022. doi: 10.14569/IJACSA.2022.0130665.
D. F. Sebastian, H. Sulistiani, and A. R. Isnain, “SENTIMENT ANALYSIS OF PUBLIC OPINION ON THE RIGHT OF INQUIRY IN INDONESIA IN 2024 USING THE SUPPORT VECTOR MACHINE (SVM) METHOD,” Jurnal Teknik Informatika (Jutif), vol. 5, no. 4, pp. 1025–1034, Jul. 2024, doi: 10.52436/1.jutif.2024.5.4.1968.
T. Widyanto, I. Ristiana, and A. Wibowo, “Komparasi Naïve Bayes dan SVM Analisis Sentimen RUU Kesehatan di Twitter,” SINTECH Journal (Science and Information Technology Journal), vol. 6, no. 3, pp. 147–161, 2023, doi: 10.31598/sintechjournal.v6i3.1433.
I. Nurma Yulita et al., “Bidirectional Long Short-Term Memory for Analysis of Public Opinion Sentiment on Government Policy During the COVID-19 Pandemic,” IJACSA) International Journal of Advanced Computer Science and Applications, vol. 14, no. 11, p. 2023, 2023, doi: 10.14569/IJACSA.2023.0141189.
A. L. Qosim, F. Kurniawan, U. Bahruddin, Z. Mubaraq, Suhartono, and M. Faisal, “Analysis Classification Opinion of Policy Government Announces Cabinet Reshuffle on YouTube Comments Using 1D Convolutional Neural Networks,” in 3rd 2021 East Indonesia Conference on Computer and Information Technology, EIConCIT 2021, Institute of Electrical and Electronics Engineers Inc., Apr. 2021, pp. 30–35. doi: 10.1109/EIConCIT50028.2021.9431884.
I. H. Hidayat, R. E. Parwanto, and Rudy, “Sentiment Analysis on the Perception and Mindset of the People of Indonesia on the Use of Vaccines to Deal with the Covid-19 Pandemic using the Text Mining Method,” in Proceedings of 2022 International Conference on Information Management and Technology, ICIMTech 2022, Institute of Electrical and Electronics Engineers Inc., 2022, pp. 57–61. doi: 10.1109/ICIMTech55957.2022.9915072.
M. A. F. Bunyamin, T. H. Pudjiantoro, F. Renaldi, and A. I. Hadiana, “Analyzing Sentiments on Indonesia’s New National Palace using The Combination of Naive Bayes and Sentiment Scoring,” in 2022 International Conference on Science and Technology, ICOSTECH 2022, Institute of Electrical and Electronics Engineers Inc., 2022. doi: 10.1109/ICOSTECH54296.2022.9829075.
A. S. Setiawan, H. H. Nuha, and M. W. A. Bawono, “Classification of Sentiment Analysis Against Omnibus Law on Twitter Social Media and News Websites Using the Naïve Bayes Method,” in 2022 5th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2022, Institute of Electrical and Electronics Engineers Inc., 2022, pp. 141–147. doi: 10.1109/ISRITI56927.2022.10052796.
M. R. Pribadi, D. Manongga, H. D. Purnomo, I. Setyawan, and Hendry, “Sentiment Analysis of the PeduliLindungi on Google Play using the Random Forest Algorithm with SMOTE,” in 2022 International Seminar on Intelligent Technology and Its Applications: Advanced Innovations of Electrical Systems for Humanity, ISITIA 2022 - Proceeding, Institute of Electrical and Electronics Engineers Inc., 2022, pp. 115–119. doi: 10.1109/ISITIA56226.2022.9855372.
M. T. Uliniansyah, A. Jarin, A. Santosa, and Gunarso, “Modeling sentiment analysis of Indonesian biodiversity policy Tweets using IndoBERTweet,” IAES International Journal of Artificial Intelligence, vol. 14, no. 3, pp. 2389–2401, Jun. 2025, doi: 10.11591/ijai.v14.i3.pp2389-2401.
G. Z. Nabiilah, I. N. Alam, E. S. Purwanto, and M. F. Hidayat, “Indonesian multilabel classification using IndoBERT embedding and MBERT classification,” International Journal of Electrical and Computer Engineering, vol. 14, no. 1, pp. 1071–1078, 2024, doi: 10.11591/ijece.v14i1.pp1071-1078.
H. Imaduddin, F. Yusfida A’la, and Y. S. Nugroho, “Sentiment Analysis in Indonesian Healthcare Applications using IndoBERT Approach,” IJACSA) International Journal of Advanced Computer Science and Applications, vol. 14, no. 8, p. 113, 2023, doi: 10.14569/IJACSA.2023.0140813.
A. Riyadi, M. Kovacs, U. Serdült, and V. Kryssanov, “IndoGovBERT: A Domain-Specific Language Model for Processing Indonesian Government SDG Documents,” Big Data and Cognitive Computing, vol. 8, no. 11, Nov. 2024, doi: 10.3390/bdcc8110153.
G. R. Usha and L. Dharmanna, “Sentiment Analysis on Business Data using Machine Learning,” in 2021 2nd International Conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2021 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2021. doi: 10.1109/ICSTCEE54422.2021.9708593.
L. Mahalakshmi and E. Anbalagan, “National Language Processing for Sentiment Analysis in Social Media-A Comprehensive Review,” in Proceedings of International Conference on Circuit Power and Computing Technologies, ICCPCT 2024, Institute of Electrical and Electronics Engineers Inc., 2024, pp. 504–508. doi: 10.1109/ICCPCT61902.2024.10672661.
S. Sindhu, S. Kumar, and A. Noliya, “A Review on Sentiment Analysis using Machine Learning,” International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2023 - Proceedings, pp. 138–142, 2023, doi: 10.1109/ICIDCA56705.2023.10099665.
R. Sharma, J. Sandhu, and V. Bharti, “Experimental Analysis of a Multimodal biometric System using Preprocessing and Feature Extraction Techniques and Their Impact on Analytical Results,” Proceedings - 2024 6th International Conference on Computational Intelligence and Communication Technologies, CCICT 2024, pp. 212–219, 2024, doi: 10.1109/CCICT62777.2024.00043.
S. A. Dzulkifli, M. N. M. Salleh, and K. H. Talpur, “Improved Weighted Learning Support Vector Machines (SVM) for High Accuracy,” ACM International Conference Proceeding Series, pp. 40–44, 2019, doi: 10.1145/3372422.3372432.
S. Thenappan, A. R. Krishnan, P. S. B. Murugan, A. S. Valarmathy, P. Chitra, and H. Sayyed, “Machine Learning Approaches for Sentiment Analysis in Social Media Data,” in 7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2023, pp. 684–688. doi: 10.1109/ICECA58529.2023.10395612.
X. Zhipeng, M. A. A. Aziz, and N. A. Razak, “Performance Evaluation of Support Vector Machine Algorithm in Object Classification Using Different Preprocessing Methods,” in 2024 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2024 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2024, pp. 169–174. doi: 10.1109/I2CACIS61270.2024.10649625.
N. Al Hafidh and A. Al-Karawi, “Advanced Sentiment Analysis of Amazon Electronics Reviews Leveraging BERT: Model Optimization and Evaluation,” in Procedia Computer Science, Elsevier B.V., 2025, pp. 3608–3618. doi: 10.1016/j.procs.2025.04.616.
V. A. Manwar and A. B. Manwar, “mBERT: A Query Refinement Model for Marathi Word Sense Disambiguation,” in 2025 IEEE International Students’ Conference on Electrical, Electronics and Computer Science, SCEECS 2025, Institute of Electrical and Electronics Engineers Inc., 2025. doi: 10.1109/SCEECS64059.2025.10940800.
S. K. Singh, A. Sharma, Sahil, D. Singh, S. Pandit, and U. Saghir, “Sentiment Analysis of English-Hindi Code-Mixed Text Using mBERT Model,” in Proceedings of the 2025 3rd International Conference on Inventive Computing and Informatics, ICICI 2025, Institute of Electrical and Electronics Engineers Inc., 2025, pp. 552–556. doi: 10.1109/ICICI65870.2025.11069692.
K. K. J, V. Saraswathi P, and R. T, “Advancing Sentiment Analysis with LLM: Comparative Study with Traditional ML on Social Media Reviews,” Institute of Electrical and Electronics Engineers (IEEE), Jul. 2025, pp. 1328–1333. doi: 10.1109/iccsp64183.2025.11088527.
Z. Yang and H. Men, “Natural Language Processing of COVID-19 Reports Involving China in New York Times - a Machine-based Framing Study of Media Language,” ACM International Conference Proceeding Series, pp. 137–143, 2022, doi: 10.1145/3582768.3582785.
F. Koto and G. Y. Rahmaningtyas, “InSet Lexicon: Evaluation of a Word List for Indonesian Sentiment Analysis in Microblogs,” IEEE, 2017. doi: 10.1109/IALP.2017.8300625.
C. P. Chai, “Comparison of text preprocessing methods,” Nat Lang Eng, vol. 29, no. 3, pp. 509–553, 2023, doi: 10.1017/S1351324922000213.
J. Li, M. S. Othman, H. Chen, and L. M. Yusuf, “Optimizing IoT intrusion detection system: feature selection versus feature extraction in machine learning,” J Big Data, vol. 11, no. 1, Dec. 2024, doi: 10.1186/s40537-024-00892-y.
I. K. D. Nuryana, L. I. D. Mawarni, and E. Juanara, “Early Detection of Environmental Issues from Social Media using IndoBERT and LDA: Case Study of Pollution and Deforestation in Indonesia,” in E3S Web of Conferences, EDP Sciences, Aug. 2025. doi: 10.1051/e3sconf/202564505005.
Y. Okumura, S. Hirokawa, and K. Takeuchi, “Significance of Low-Frequent Words in Concept Describing Document,” Proceedings - 2019 8th International Congress on Advanced Applied Informatics, IIAI-AAI 2019, pp. 1035–1036, 2019, doi: 10.1109/IIAI-AAI.2019.00214.
E. S. Alamoudi and N. S. Alghamdi, “Sentiment classification and aspect-based sentiment analysis on yelp reviews using deep learning and word embeddings,” J Decis Syst, vol. 30, no. 2–3, pp. 259–281, 2021, doi: 10.1080/12460125.2020.1864106.
N. Keivandarian and M. Carvalho, “A Survey on Sentiment Classification Methods and Challenges,” in NAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference, Association for Computational Linguistics (ACL), 2019, pp. 4171–4186. doi: 10.32473/flairs.36.133314.
T. J. Sefara, M. Mbooi, K. Mashile, T. Rambuda, and M. Rangata, “A Toolkit for Text Extraction and Analysis for Natural Language Processing Tasks,” in 5th International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems, icABCD 2022 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2022. doi: 10.1109/icABCD54961.2022.9856269.
T. Zhang and R. Zhang, “Revealing the power of BERT for text sentiment classification,” in 4th IEEE International Conference on Automation, Electronics and Electrical Engineering, AUTEEE 2021, Institute of Electrical and Electronics Engineers Inc., 2021, pp. 14–17. doi: 10.1109/AUTEEE52864.2021.9668704.
H. Pal and B. Bhushan, “Sentiment Analysis on Twitter Dataset using Voting Classifier,” in 2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024, Institute of Electrical and Electronics Engineers Inc., 2024. doi: 10.1109/ICEECT61758.2024.10739316.
J. Görtler, F. Hohman, D. Moritz, M. Kirchner, and K. Patel, “Confusion matriks,” Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology, May 2022, doi: 10.1145/3491102.3501823.
N. A. P. Masaling, T. Lubis, A. Amalia, A. R. Lubis, and M. S. Lydia, “Category Based Sentiment Analysis for Basic Skin-Cares Using LDA and SVM Approach,” Proceeding - ELTICOM 2022: 6th International Conference on Electrical, Telecommunication and Computer Engineering 2022, pp. 182–189, 2022, doi: 10.1109/ELTICOM57747.2022.10037876.
S. Yerramilli and D. W. Apley, “Fractional Cross-Validation for Optimizing Hyperparameters of Supervised Learning Algorithms,” Technometrics, 2025, doi: 10.1080/00401706.2025.2515926.
S. A. Ajagbe, J. B. Awotunde, and H. Florez, “Intrusion Detection: A Comparison Study of Machine Learning Models Using Unbalanced Dataset,” SN Comput Sci, vol. 5, no. 8, 2024, doi: 10.1007/s42979-024-03369-0.
I. Nurma Yulita et al., “Bidirectional Long Short-Term Memory for Analysis of Public Opinion Sentiment on Government Policy During the COVID-19 Pandemic,” 2023. [Online]. Available: www.ijacsa.thesai.org
S. Lee, R. H. Ali, T. A. Khan, M. Karunanithi, I. Ahmad, and R. Kouatly, “Analysing Public Perception of South Korea’s Low Birth Rate Policies Using NLP-based Sentiment Analysis,” in SIST 2025 - 2025 IEEE 5th International Conference on Smart Information Systems and Technologies, Conference Proceedings, Institute of Electrical and Electronics Engineers Inc., 2025. doi: 10.1109/SIST61657.2025.11139252.
F. Akter, T. T. Aurpa, F. Islam, M. S. Islam, Hossneara, and M. Ashrafuzzaman, “Sentiment Analysis of Public Perception Toward Mob Justice,” in 2025 IEEE International Conference on Quantum Photonics, Artificial Intelligence, and Networking, QPAIN 2025, Institute of Electrical and Electronics Engineers Inc., 2025. doi: 10.1109/QPAIN66474.2025.11171704.
P. Sankireddy, N. S. Ramireddy, A. R. Penumada, J. Pechetti, and N. C. Nair, “Analyzing Public Feedback on Urban Infrastructure Projects,” in 2025 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2025, Institute of Electrical and Electronics Engineers Inc., 2025. doi: 10.1109/IATMSI64286.2025.10984847.
V. Joshi, N. Yede, P. Parihar, C. Dhule, R. Agrawal, and N. C. Morris, “Quantifying Effectiveness of Governmental Agriculture Policies using RNN-Transformer based Sentiment Analysis,” in 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025, Institute of Electrical and Electronics Engineers Inc., 2025, pp. 1–7. doi: 10.1109/IDCIOT64235.2025.10915148.
A. Soreng and K. C. Bandhu, “A Bi-LSTM and Attention-Based Sentiment Classifier for Enhancing Public Trust in COVID-19 Vaccination,” in 2025 World Skills Conference on Universal Data Analytics and Sciences, WorldSUAS 2025, Institute of Electrical and Electronics Engineers Inc., 2025. doi: 10.1109/WorldSUAS66815.2025.11199276.
W. R. E. Putri, S. Suhendro, R. Azhar, N. Desriani, and A. C. Pramana, “Exploring Public Sentiment on Green Economy Policy: A Natural Language Processing-Based Analysis,” International Journal of Energy Economics and Policy, vol. 15, no. 2, pp. 560–565, Feb. 2025, doi: 10.32479/ijeep.18360.
Additional Files
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Yulia Kurniawati, Ricky Bahari Hamid, Dana Indra Sensuse, Sofian Lusa, Prasetyo Adi Wibowo Putro, Sofiyanti Indriasari
This work is licensed under a Creative Commons Attribution 4.0 International License.
