Peran Sentiment Analysis dalam Business Intelligence dan Pengambilan Keputusan Berbasis Data
DOI:
https://doi.org/10.61132/jupiter.v3i4.1450Keywords:
Business Intelligence, Customer Reviews, Data-Driven Decision-Making, Marketing Analytics, Sentiment AnalysisAbstract
Rapid growth of digital platforms has generated large volumes of unstructured data, particularly customer reviews, social media comments, and online opinions, which contain valuable insights for organizations. This study aims to examine the role of sentiment analysis in Business Intelligence and data-driven decision-making. The method used is a narrative literature review by analyzing 30 selected journal articles related to sentiment analysis, business intelligence, decision-making, customer reviews, marketing analytics, and data-driven culture. The findings show that sentiment analysis plays an important role in transforming textual data into strategic information that can support customer understanding, marketing decisions, product development, customer relationship management, reputation monitoring, and strategic planning. The integration of sentiment analysis into Business Intelligence enables organizations to not only understand what happens through numerical data, but also why it happens through customer opinions and emotions. However, its implementation still faces challenges related to data quality, informal language, sarcasm, algorithmic bias, model interpretability, and human validation. This study implies that sentiment analysis can strengthen modern Business Intelligence and support more responsive, objective, and evidence-based managerial decision-making.
References
Adak, A., Pradhan, B., & Shukla, N. (2022). Sentiment Analysis of Customer Reviews of Food Delivery Services Using Deep Learning and Explainable Artificial Intelligence: Systematic Review. Foods, 11(10), 1–16. https://doi.org/10.3390/foods11101500
Aguilar-Moreno, J. A., Palos-Sanchez, P. R., & Pozo-Barajas, R. (2024). Sentiment analysis to support business decision-making. A bibliometric study. AIMS Mathematics, 9(2), 4337–4375. https://doi.org/10.3934/math.2024215
Alahmadi, K., Alharbi, S., Chen, J., & Wang, X. (2025). Generalizing Sentiment Analysis: a Review of Progress, Challenges, and Emerging Directions. Social Network Analysis and Mining, 15(1), 1–28. https://doi.org/10.1007/s13278-025-01461-8
Alawamleh, H. A. M., Alshweesh, R. G., Abu-darwish, N. J. S., Aljundi, A. hussein, & Salah, A. A. (2025). Influence of Business Intelligence on Organizational Performance: The Moderating Role of Employee BI Experiences. Administrative Sciences, 16(2), 1–17. https://doi.org/10.3390/admsci16020100
Ashbaugh, L., & Zhang, Y. (2024). A Comparative Study of Sentiment Analysis on Customer Reviews Using Machine Learning and Deep Learning. Computers, 13(12), 1–16. https://doi.org/10.3390/computers13120340
Capuano, N., Greco, L., Ritrovato, P., & Vento, M. (2021). Sentiment Analysis for Customer Relationship Management: an Incremental Learning Approach. Applied Intelligence, 51(6), 3339–3352. https://doi.org/10.1007/s10489-020-01984-x
Ciocodeică, D. F., Chivu, R. G. P., Popa, I. C., Mihălcescu, H., Orzan, G., & Băjan, A. M. D. (2022). The Degree of Adoption of Business Intelligence in Romanian Companies—The Case of Sentiment Analysis as a Marketing Analytical Tool. Sustainability (Switzerland), 14(12), 1–20. https://doi.org/10.3390/su14127518
Cui, J., Wang, Z., Ho, S. B., & Cambria, E. (2023). Survey on Sentiment Analysis: Evolution of Research Methods and Topics. In Artificial Intelligence Review (Vol. 56, Issue 8). Springer Netherlands. https://doi.org/10.1007/s10462-022-10386-z
Ghatora, P. S., Hosseini, S. E., Pervez, S., Iqbal, M. J., & Shaukat, N. (2024). Sentiment Analysis of Product Reviews Using Machine Learning and Pre-Trained LLM. Big Data and Cognitive Computing, 8(12), 1–18. https://doi.org/10.3390/bdcc8120199
Grande-Ramírez, J. R., Roldán-Reyes, E., Aguilar-Lasserre, A. A., & Juárez-Martínez, U. (2022). Integration of Sentiment Analysis of Social Media in the Strategic Planning Process to Generate the Balanced Scorecard. Applied Sciences (Switzerland), 12(23), 1–19. https://doi.org/10.3390/app122312307
Hamed, T., & Madanchian, M. (2023). Artificial Intelligence and Sentiment Analysis : A Review in Competitive Research. Computers 2023, 12, 37., 12(37), 1–15. https://doi.org/https:// doi.org/10.3390/computers12020037
Herrera-Poyatos, D., Peláez-González, C., Zuheros, C., Herrera-Poyatos, A., Tejedor, V., Herrera, F., & Montes, R. (2025). An Overview of Model Uncertainty and Variability in LLM-based Sentiment Analysis: Challenges, Mitigation Strategies, and the Rof Explainability. Frontiers in Artificial Intelligence, 8(August), 1–24. https://doi.org/10.3389/frai.2025.1609097
Hua, Y. C., Denny, P., Wicker, J., & Taskova, K. (2024). A Systematic Review of Aspect-based Sentiment Analysis: Domains, Methods, and Trends. In Artificial Intelligence Review (Vol. 57, Issue 11). Springer Netherlands. https://doi.org/10.1007/s10462-024-10906-z
Hurbean, L., Militaru, F., Munteanu, V. P., Danaiata, D., Fotache, D., & Muntean, M. (2025). Assessing the Influence of Business Intelligence and Analytics and Data-Driven Culture on Managerial Performance: Evidence from Romania. Systems, 13(1), 1–20. https://doi.org/10.3390/systems13010002
Iqbal, A., Amin, R., Iqbal, J., Alroobaea, R., Binmahfoudh, A., & Hussain, M. (2022). Sentiment Analysis of Consumer Reviews Using Deep Learning. Sustainability (Switzerland), 14(17), 1–19. https://doi.org/10.3390/su141710844
Kahya Özyirmidokuz, E., Molu Elmas, B., & Stoica, E. A. (2025). AI-Based Sentiment Analysis of E-Commerce Customer Feedback: A Bilingual Parallel Study on the Fast Food Industry in Turkish and English. Journal of Theoretical and Applied Electronic Commerce Research , 20(4), 1–34. https://doi.org/10.3390/jtaer20040294
Karasenko, A., & Baier, D. (2025). Beyond Sentiment Analysis of Online Customer Reviews: an Approach to Automated Measurement of Technology Acceptance from Online Customer Reviews. In Journal of Business Economics (Vol. 95, Issue 7). Springer Berlin Heidelberg. https://doi.org/10.1007/s11573-025-01232-z
Kauffmann, E., Peral, J., Gil, D., Ferrández, A., Sellers, R., & Mora, H. (2019). Managing Marketing Decision-making with Sentiment Analysis: An Evaluation of the Main Product Features Using Text Data Mining. Sustainability (Switzerland), 11(15), 1–19. https://doi.org/10.3390/su11154235
Lee, C. Y., & Anderl, E. (2025). Does Business News Sentiment Matter in the Energy Stock Market? Adopting Sentiment Analysis for Short-term Stock Market Prediction in the Energy Industry. Frontiers in Artificial Intelligence, 8(July), 1–6. https://doi.org/10.3389/frai.2025.1559900
Ligthart, A., Catal, C., & Tekinerdogan, B. (2021). Systematic Reviews in Sentiment Analysis: a Tertiary Study. In Artificial Intelligence Review (Vol. 54, Issue 7). Springer Netherlands. https://doi.org/10.1007/s10462-021-09973-3
Mekimah, S., Zighed, R., Mili, K., & Bengana, I. (2024). Business Intelligence in Organizational Decision-making: a Bibliometric Analysis of Research Trends and Gaps (2014–2024). Discover Sustainability, 5(1), 1–18. https://doi.org/10.1007/s43621-024-00692-7
Moleong, L. J. (2017). Metodologi Penelitian Kualitatif. Remaja Rosdakarya.
Nichifor, E., Brătucu, G., Chițu, I. B., Lupșa-Tătaru, D. A., Chișinău, E. M., Todor, R. D., Albu, R. G., & Bălășescu, S. (2023). Utilising Artificial Intelligence to Turn Reviews into Business Enhancements through Sentiment Analysis. Electronics (Switzerland), 12(21), 1–19. https://doi.org/https://doi.org/10.3390/electronics12214538
Rokade, P. P., & Aruna Kumari, D. (2019). Business Intelligence Analytics Using Sentiment Analysis-a Survey. International Journal of Electrical and Computer Engineering, 9(1), 613–620. https://doi.org/10.11591/ijece.v9i1.pp613-620
Saleh, L., & Semaan, S. (2024). The Potential of AI in Performing Financial Sentiment Analysis for Predicting Entrepreneur Survival. Administrative Sciences, 14(9), 1–12. https://doi.org/10.3390/admsci14090220
Saura, J. R., Palos-Sanchez, P., & Grilo, A. (2019). Detecting indicators for startup business success: Sentiment analysis using text data mining. Sustainability (Switzerland), 11(3), 1–14. https://doi.org/10.3390/su11030917
Shafiezad, O., & Mostofi, H. (2024). Sentiment Analysis of Berlin Tourists’ Food Quality Perception Through Artificial Intelligence. Tourism and Hospitality, 5(4), 1396–1417. https://doi.org/10.3390/tourhosp5040078
Solechan, A., & Abidin, R. (2024). Implementasi Teknologi Blockchain dalam Digital Marketing. Jurnal Informatika Upgris, 10(2), 23–28. https://doi.org/10.26877/jiu.v10i2.21002
Solechan, A., Wijanarko, T. A., & Hartono, B. (2023). Transformasi Digital Pada UMKM Dalam Meningkatkan Daya Saing Pasar. Jurnal Informatika UPGRIS, 9(1), 7–12.
Sugiyono. (2019). Metodelogi Penelitian Kuantitatif dan Kualitatif Dan R&D. Alfabeta.
Susanto, H., Omar, A. S., Shafa Susanto, A. K., Setiana, D., Fang-Yie, L., Shaikh, J. M., Insani, A., Khusni, U., Hidayat, R., Akbari, I., & Basuki, I. (2025). Toward Robust Social Media Sentiment for SMEs: a Comparative Study of Dictionary-based and Machine Learning Approaches with Insights for Hybrid Methodologies. Frontiers in Big Data, 8(April), 1–21. https://doi.org/10.3389/fdata.2025.1594374
Wangsa, J. I. P., Agung, Y. J., Rahmi, S. R., Murfi, H., Hariadi, N., Nurrohmah, S., Satria, Y., & Za’in, C. (2025). Large Language Model-Based Topic-Level Sentiment Analysis for E-Grocery Consumer Reviews. Big Data and Cognitive Computing, 9(8), 1–19. https://doi.org/10.3390/bdcc9080194
Wen, Z., Chen, Y., Liu, H., & Liang, Z. (2024). Text Mining Based Approach for Customer Sentiment and Product Competitiveness Using Composite Online Review Data. Journal of Theoretical and Applied Electronic Commerce Research , 19(3), 1776–1792. https://doi.org/10.3390/jtaer19030087
Yang, M., Jiang, B., Wang, Y., Hao, T., & Liu, Y. (2022). News Text Mining-Based Business Sentiment Analysis and Its Significance in Economy. Frontiers in Psychology, 13(July), 1–7. https://doi.org/10.3389/fpsyg.2022.918447
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