BUSINESS INTELLIGENCE APPROACH TO MARKET RESEARCH ON FOOD COMMODITY BY USING BIG DATA ANALYSIS, CASE STUDY: FORUM JUAL BELI KASKUS
Keywords:
Business Intelligence, Big Data Analysis, e-Commerce, Market Research, Food ControlAbstract
The emerging Covid-19 pandemic and the increasing use of internet access trend in Indonesia have successfully changed most consumer behaviour, shifting into the online market (e-Commerce) instead of the conventional physical market. It also presents vast new business opportunities, particularly in the food and beverage industries. However, the authority must transform the decision support system based on data-driven consideration to ensure consumer protection. This research aimed to present the Big Data Analysis power to find insight from the current condition of e- commerce by conducting market research on Forum Jual Beli Kaskus, which was chosen due to less restriction while implementing the self-programming web scrape engine. The collected data is then analyzed through Rapidminer software for text preprocessing and predictive models shown in the Business intelligence tool. Authors find that the web scraping engine successfully collected the whole population of products listed in the food and beverages category as well as text preprocessing resulted in several keywords which represent the product trend. The prediction model achieved 99.85% accuracy and a minimum 80% precision class while the test dataset was introduced to confirm and test the model. By utilizing The Big Data Analysis and Business Intelligence Tools, the government authority could catch enormous insight based on the real-time market research process to formulate a better policy approach.
Downloads
References
Widjaja, F., et al., Factors Influencing Consumers’ Continuance Intention to Use Mobile Payments in Indonesia. Psychology and Education Journal, 2020. 57(9): p. 2293-2301.
Matuan, A., et al., Factors Affecting Provision of Basic Immunization for Infants at Popukoba Health Center, Jayawijaya, Papua. Journal of Natural Science, Biology and Medicine, 2021. 12(3).
Sakai, M. and B. Anoraga, Education, digital enterprise and Islam in the Indonesian modern embedded economy, in Muslim Women in the Economy. 2020, Routledge. p. 214-228.DOI: https://doi.org/10.4324/9780429263194-13.
Gerritsen, S., et al., The timing, nature and extent of social media marketing by unhealthy food and drinks brands during the COVID-19 pandemic in New Zealand. Frontiers in Nutrition, 2021. 8: p. 65.DOI: https://doi.org/10.3389/fnut.2021.645349.
Kotsanopoulos, K.V. and I.S. Arvanitoyannis, The role of auditing, food safety, and food quality standards in the food industry: A review. Comprehensive Reviews in Food Science and Food Safety, 2017. 16(5): p. 760-775.DOI: https://doi.org/10.1111/1541-4337.12293.
McFadden, D., The choice theory approach to market research. Marketing science, 1986. 5(4): p. 275-297.DOI: https://doi.org/10.1287/mksc.5.4.275.
Kirkpatrick, R., Big data for development. Big Data, 2013. 1(1): p. 3-4.DOI: https://doi.org/10.1089/big.2012.1502.
Parks, R., W. Ceccucci, and R. McCarthy, Harnessing business analytics: Analyzing data analytics programs in US business schools. Information Systems Education Journal, 2018. 16(3): p. 15.
Sulisworo, D., R. Nasir, and I. Maryani, Identification of teachers’ problems in Indonesia on facing global community. International Journal of Research Studies in Education, 2017. 6(2): p. 81-90.DOI: https://doi.org/10.5861/ijrse.2016.1519.
Fan, S., R.Y.K. Lau, and J.L. Zhao, Demystifying big data analytics for business intelligence through the lens of marketing mix. Big Data Res. 2 (1), 28–32 (2015).
Mohthash, M.T., S.K. Shah, and A. Thirupathi, KRAS gene polymorphism (rs61764370) and its impact on breast cancer risk among women in kerala population, South India. Journal of Natural Science, Biology and Medicine, 2020. 11(2): p. 140.
Han, S. and C.K. Anderson, Web scraping for hospitality research: Overview, opportunities, and implications. Cornell Hospitality Quarterly, 2021. 62(1): p. 89-104.DOI: https://doi.org/10.1177/1938965520973587.
Milev, P., Conceptual approach for development of web scraping application for tracking information. Economic Alternatives, 2017(3): p. 475-485.
Wöber, K.W., Standardizing city tourism statistics. Annals of Tourism Research, 2000. 27(1): p. 51-68.DOI: https://doi.org/10.1016/S0160-7383(99)00054-7.
Debortoli, S., O. Müller, and J. vom Brocke, Comparing business intelligence and big data skills. Business & Information Systems Engineering, 2014. 6(5): p. 289-300.DOI: https://doi.org/10.1007/s12599-014-0344-2.
Alnoukari, M., From Business Intelligence to Big Data: The Power of Analytics, in Research Anthology on Big Data Analytics, Architectures, and Applications. 2022, IGI Global. p. 823- 841.DOI: https://doi.org/10.4018/978-1-6684-3662-2.ch038.
Passlick, J., B. Lebek, and M.H. Breitner, A self-service supporting business intelligence and big data analytics architecture. 2017: p. 1126-1140.
Sun, Z., H. Zou, and K. Strang. Big data analytics as a service for business intelligence. Springer.DOI: https://doi.org/10.1007/978-3-319-25013-7_16.
Marasanapalle, J., et al. Business intelligence from Twitter for the television media: a case study. IEEE.DOI: https://doi.org/10.1109/BASNA.2010.5730304.
Driscoll, K. and S. Walker, Big data, big questions| working within a black box: Transparency in the collection and production of big twitter data. International Journal of Communication, 2014. 8: p. 20.
Panjwani, D., et al., A novel behavioral model in initiation and sustenance of toothbrushing behavior among dental and medical students in India: an exploratory analysis. Journal of Natural Science, Biology and Medicine, 2021. 12(2): p. 149-149.
Saurkar, A.V., K.G. Pathare, and S.A. Gode, An overview on web scraping techniques and tools. International Journal on Future Revolution in Computer Science & Communication Engineering, 2018. 4(4): p. 363-367.
Friedman, B. and H. Nissenbaum, Bias in computer systems. ACM Transactions on Information Systems (TOIS), 1996. 14(3): p. 330-347.DOI: https://doi.org/10.1145/230538.230561.
Hirschey, J.K., Symbiotic relationships: Pragmatic acceptance of data scraping. Berkeley Tech. LJ, 2014. 29: p. 897.DOI: https://doi.org/10.2139/ssrn.2419167.
Slamet, C., et al. Web scraping and Naïve Bayes classification for job search engine. IOP Publishing.DOI: https://doi.org/10.1088/1757-899X/288/1/012038.
Ramaditya, M. Exploring the impact of perception after rebranding and customer satisfaction on corporate image (A case study: PT. Darta Media Indonesia Kaskus). Atlantis Press.DOI: https://doi.org/10.2991/aicmar-18.2019.38.
Djufri, M., PENERAPAN TEKNIK WEB SCRAPING UNTUK PENGGALIAN POTENSI PAJAK (STUDI KASUS PADA ONLINE MARKET PLACE TOKOPEDIA, SHOPEE DAN BUKALAPAK). Jurnal BPPK: Badan Pendidikan dan Pelatihan Keuangan, 2020. 13(2): p. 65-75.DOI: https://doi.org/10.48108/jurnalbppk.v13i2.636.
de Souza, J.G.C., et al. Generating e-commerce product titles and predicting their quality.
Arslan, A. and O. Yilmazel. An Approach to Prevent Stemming Side Effects in Information Retrieval.
Mäkelä, P. and E. Österberg, Weakening of one more alcohol control pillar: a review of the effects of the alcohol tax cuts in Finland in 2004. Addiction, 2009. 104(4): p. 554-563.DOI: https://doi.org/10.1111/j.1360-0443.2009.02517.x.
Ruzgas, T. and J. Dabulytė-Bagdonavičienė, Business Intelligence for Big Data Analytics. International Journal of Computer Applications Technology and Research, 2017. 6(1): p. 001- 008.DOI: https://doi.org/10.7753/IJCATR0601.1001.
Downloads
Published
Issue
Section
License
Copyright (c) 2022 Author
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
