Machine Learning and Blockchain Integration for RealTime Sentiment Analysis and Digital Rupiah Ecosystem

Ninda Lutfiani; Umi Rusilowati; Steven Harazaki Lase; Kristina Vaher

Authors

Ninda Lutfiani Satya Wacana Christian University, Indonesia Author https://orcid.org/0000-0001-7019-0020
Umi Rusilowati Pamulang University, Indonesia Author https://orcid.org/0000-0001-7134-532X
Steven Harazaki Lase University of Raharja, Indonesia Author https://orcid.org/0009-0002-6647-7541
Kristina Vaher iLearning Group, Estonia Author https://orcid.org/0009-0009-6790-0680

Keywords:

Sentiment Analysis, Digital Currency, Machine Learning, Viewer Engagement, Digital Media

Abstract

The rapid growth of digital streaming platforms and global online communities has significantly increased the volume of user generated content, making it difficult for organizations to understand viewer engagement trends in real time. This study develops and evaluates machine learning models for real time sentiment analysis to identify global viewer engagement patterns across large scale digital data streams. Analytical framework is employed by collecting viewer comments and interaction data from multiple online platforms, followed by preprocessing techniques including text normalization, tokenization, and feature extraction. Several machine learning algorithms, including supervised classification models and natural language processing techniques, are trained and evaluated to detect positive, negative, and neutral sentiments in real time. Model performance is assessed using accuracy, precision, recall, and F1 score to determine the most effective approach for large scale sentiment monitoring. The findings demonstrate that optimized machine learning models significantly improve the accuracy and responsiveness of real time sentiment detection, enabling more reliable identification of global viewer engagement trends and behavioral patterns. The integration of automated sentiment analysis also enhances the capability of organizations to process large volumes of streaming textual data efficiently. This research highlights the importance of machine learning driven sentiment analysis systems as strategic tools for understanding global audience engagement, supporting data driven decision making, and improving adaptive content strategies in rapidly evolving digital media environments.

Downloads

Download data is not yet available.

References

[1] Kementerian Komunikasi dan Informatika Republik Indonesia, “Digital transformation and artificial intelligence development strategy in indonesia,” Kominfo RI, Jakarta, Tech. Rep., 2023.

[2] Badan Riset dan Inovasi Nasional, “Artificial intelligence and big data development roadmap for indonesia’s digital ecosystem,” BRIN, Jakarta, Tech. Rep., 2024.

[3] R. Zheng and H. Liu, “Attention mechanism enhancements for sentiment analysis tasks in complex and context-rich natural language processing scenarios,” Neurocomputing, 2024.

[4] M. Islam and T. Karim, “Hybrid natural language processing models combining rule-based and deep learning approaches for sentiment classification tasks,” Applied Soft Computing, 2025.

[5] R. Gupta and D. Sharma, “Distributed computing approaches for scalable sentiment analysis across large and continuously evolving social media datasets,” Journal of Big Data, 2025.

[6] H. Lee and M. Park, “Context-aware sentiment analysis models leveraging deep contextual embeddings for improved opinion mining performance,” IEEE Access, 2025.

[7] S. Anwar and U. Malik, “Big data driven natural language processing techniques for efficient sentiment analysis in real-time applications,” Information Sciences, 2025.

[8] A. Saputra and B. Wijaya, “Sentiment analysis of indonesian social media using deep learning and transformer-based contextual embedding techniques,” Procedia Computer Science, 2025.

[9] J. Park and D. Kim, “Deep contextual learning approaches for improving sentiment analysis accuracy in complex and highly dynamic social media environments,” Information Processing & Management, 2024.

[10] W. Liu and H. Chen, “Streaming natural language processing systems for real-time sentiment analysis in large-scale distributed data processing environments,” Future Generation Computer Systems, 2024.

[11] P. Hernandez and M. Ruiz, “Artificial intelligence based sentiment analysis techniques for understanding user behavior in large-scale digital social platforms,” Technological Forecasting and Social Change, 2024.

[12] S. Kim and J. Lee, “Attention-enhanced transformer models for capturing fine-grained sentiment patterns in complex natural language processing tasks,” Neurocomputing, 2024.

[13] K. Zhou and R. Wang, “Multimodal sentiment analysis integrating textual and visual features using deep learning architectures for social media applications,” Information Sciences, 2024.

[14] M. Rahman and F. Karim, “Real-time multilingual sentiment analysis using transformer-based architectures for global social media monitoring systems,” IEEE Access, 2025.

[15] T. Nguyen and L. Pham, “Large-scale opinion mining and sentiment classification using deep neural networks and distributed computing frameworks,” Journal of Big Data, 2025.

[16] N. Singh and A. Verma, “Contextual large language model approaches for sentiment analysis in noisy and unstructured real-world textual datasets,” Expert Systems with Applications, 2025.

[17] D. Garcia and M. Lopez, “Advanced text analytics and sentiment analysis techniques using hybrid artificial intelligence models in digital ecosystems,” Technovation, 2025.

[18] B. Wijaya and A. Saputra, “Transformer-based sentiment analysis for indonesian language datasets using contextual embeddings and deep learning techniques,” Procedia Computer Science, 2025.

[19] L. Chen and J. Wu, “Aspect-based sentiment analysis using deep learning techniques for fine-grained opinion mining in complex textual datasets,” Knowledge-Based Systems, 2025.

[20] R. Almeida and B. Santos, “Sarcasm detection in social media texts to enhance sentiment analysis accuracy using transformer-based deep learning models,” Expert Systems with Applications, 2025.

[21] A. Verma and K. Singh, “Domain adaptation techniques for improving sentiment analysis performance across heterogeneous and cross-domain textual datasets,” Information Sciences, 2025.

[22] L. Huang and M. Zhang, “Explainable artificial intelligence approaches for interpreting sentiment analysis results in complex and high-dimensional natural language processing models,” IEEE Access, 2025.

[23] J. Devlin and M. Chang, “A comprehensive review of bert and its applications in sentiment analysis,”ACM Computing Surveys, 2024.

[24] T. Brown and B. Mann, “Large language models for natural language understanding: Trends and challenges,” Nature Machine Intelligence, 2024.

[25] T. Mikolov, “Advances in neural language models for sentiment classification,” IEEE Transactions on Neural Networks and Learning Systems, 2024.

[26] A. Vaswani, “Transformer architectures in large-scale sentiment analysis systems,” Information Processing & Management, 2025.

[27] A. Radford, “Generative pre-trained transformers for sentiment analysis and text mining,” Expert Systems with Applications, 2025.

[28] I. Goodfellow, “Deep learning advancements in natural language processing,” Neurocomputing, 2025.

[29] A. S. Rafika, I. Faridah, and A. A. Sangaji, “Kkn management center and region development research institutions and community devotion state university of yogyakarta,” APTISI Transactions on Management, vol. 1, no. 1, pp. 61–69, 2017.

[30] A. D. Dwipayana, N. L. Darmayanti, and B. Wijonarko, “Challenges and opportunities for leadership and talent development graduates of cadets,” ADI Journal on Recent Innovation, vol. 4, no. 2, pp. 122–127, 2023.

[31] E. D. Safitri, S. R. P. Junaedi, and A. Priono, “Swot analysis is used in the startup business development strategy,” Startupreneur Business Digital (SABDA Journal), vol. 2, no. 2, pp. 136–142, 2023.

[32] R. Sivaraman, M.-H. Lin, M. I. C. Vargas, S. I. S. Al-Hawary, U. Rahardja, F. A. H. Al-Khafaji, E. V. Golubtsova, and L. Li, “Multi-objective hybrid system development: To increase the performance of diesel/photovoltaic/wind/battery system.” Mathematical Modelling of Engineering Problems, vol. 11, no. 3, 2024.

[33] J. Siswanto, U. Rahardja, I. Sembiring, K. D. Hartomo, H. D. Purnomo, A. Iriani et al., “Number of road accidents predicting using deep learning-based lstm development models,” in 2023 11th International Conference on Cyber and IT Service Management (CITSM). IEEE, 2023, pp. 1–6.

[34] I. Puspitasari, F. Rusydi, N. A. Iahad, and A. Yasin, “A comparative analytical approach for predicting continuance intention in mutual fund investment apps,” Aptisi Transactions on Technopreneurship (ATT), vol. 7, no. 3, pp. 1052–1065, 2025.

[35] U. Rahardja, Q. Aini, and A. Khoirunisa, “Implementasi business intelligence menggunakan highchart pada sistem penilaian absensi berbasis yii framework,” CSRID (Computer Science Research and Its Development Journal), vol. 9, no. 2, pp. 115–124, 2017.

[36] P. Sunarya, U. Rahardja, and D. Desrianti, “Development assessment module portfolio e-imei students with learning to improve the quality of concentration case study mavib,” vol, vol. 13, pp. 3597–3606, 2016.

[37] D. Gathmyr, U. Suhud, H. Herlitah, H. Hamidah, R. T. H. Safariningsih, and J. Wilson, “Technological advancements in perceived organizational support enhancing healthcare systems towards sustainable development goals,” Aptisi Transactions on Technopreneurship (ATT), vol. 7, no. 2, pp. 516–526, 2025.

[38] N. S. M. K. M. D. B. PA Sunarya, U Rahardja, “Impact of waterfall method on systematic academic information system development,” Technomedia Journal, vol. 9, no. 3, pp. 360–372, 2025.

[39] M. S. Mardiyanto, U. Rahardja, E. Susanti et al., “Z-fundraising project research development (zford) sebagai media crowdfunding,” Journal Sensi: Strategic of Education in Information System, vol. 2, no. 1,

pp. 45–57, 2016.

[40] A. S. Bein and A. Williams, “Development of deep learning algorithms for improved facial recognition in security applications,” IAIC Transactions on Sustainable Digital Innovation (ITSDI), vol. 5, no. 1, pp. 19–23, 2023.

[41] E. Guustaaf, U. Rahardja, Q. Aini, H. W. Maharani, and N. A. Santoso, “Blockchain-based education

project,” Aptisi Transactions on Management, vol. 5, no. 1, pp. 46–61, 2021.

[42] M. G. Hardini, N. A. Yusuf, A. R. A. Zahra et al., “Convergence of intelligent networks: Harnessing the power of artificial intelligence and blockchain for future innovations,” ADI Journal on Recent Innovation, vol. 5, no. 2, pp. 200–209, 2024.

[43] D. Septyawati, S. Suroso, S. Bhupathiraju, C. T. Hua, and A. Fitriani, “Blockchain technology integration for enhancing security and reliability in modern information systems,” International Transactions on Artificial Intelligence, vol. 4, no. 1, pp. 95–104, 2025.

[44] A. Maariz, M. A. Wiputra, and M. R. D. Armanto, “Blockchain technology: Revolutionizing data integrity and security in digital environments,” International Transactions on Education Technology (ITEE), vol. 2,no. 2, pp. 92–98, 2024.

[45] D. Cahyadi, A. Faturahman, H. Haryani, E. Dolan, and S. Millah, “Bcs: Blockchain smart curriculum system for verification student accreditation,” International Journal of Cyber and IT Service Management, vol. 1, no. 1, pp. 65–83, 2021.

[46] T. Green, H. R. Chakim, D. Andayani, U. Rusilowati et al., “Implementation of blockchain in academic data management,” Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi, vol. 4, no. 1, pp. 74–82, 2025.

[47] K. Mahfudi, E. N. Fadillah, T. S. Goh, E. Fitriyanti, L. Aroha et al., “Transformasi manajemen pendidikan tinggi berbasis data dan blockchain: Transformation of higher education management based on data and blockchain,” Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi, vol. 3, no. 2, pp. 127–136, 2025.

[48] S. L. D. Pramesti, Y. I. Tanjung, A. Aulia, M. R. Ramadhan, and I. Van Versie, “Migration of blockchain systems to quantum resistant security ecdsa vs nist mldsa,” Blockchain Frontier Technology, vol. 5, no. 2, pp. 207–218, 2026.

[49] I. K. Gunawan, H. T. Sukmana, A. Y. Ardianto et al., “Blockchain technology as a media for sharing information that generates user access rights and incentives,” Blockchain Frontier Technology, vol. 1, no. 01, pp. 44–55, 2021.

[50] M. Akhyar, T. M. F. Yoga, and E. Sukmawati, “Blockchain-based ubiquitous learning method towards higher education security,” Blockchain Frontier Technology, vol. 1, no. 2, pp. 44–54, 2022.