Business Intelligence Dashboard Implementation on a Travel Agency in Jakarta
( Vol-4,Issue-6,June 2017 )

Euis Nina Saparina Yuliani, Heru Subawanto, Anggi Oktaviani


Business Intelligence, Dashboard, Data Mining, Strategy, Travel Agency.


Information is growing at an alarming rate. As the development of information, organizations need to manage them and make them can be processed are growing as well. So this makes the problem to get the right information at the right place for the right people. And this fact is important for the company to be successful. This is what causes the Business Intelligence (BI) in the preferences of today's technology. BI is a process from raw data to be read. BI solutions help transform raw data into actionable information that can help support business decision making. This can help companies develop new opportunities. By identifying new opportunities and implement effective strategies, it will result in a competitive market advantage and stable in the long term. In this study, analysis and visualization of large amounts of data from a travel agency in Jakarta to help make the right business decisions using BI tools.

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[1] Bose, R. : Advanced analytics: Opportunities and challenges. Industrial Management & Data Systems, 109(2), 155-172. (2009)
[2] Chen, H., Chiang, R. H., & Storey, V. C. : Business Intelligence and Analytics: From Big Data to Big Impact. MIS quarterly, 36(4), 1165-1188. (2012)
[3] Cios, K. J., Pedrycz, W., & Swiniarski, R. W. : Data Mining and Knowledge Discovery. In Data Mining Methods for Knowledge Discovery (pp. 1-26). Springer USA. (2007)
[4] El Deen, M. A., & Solayman, M. M. : Maximizing strategic performance results: Adopting balanced scorecards and BI tools. International Journal of Computer Applications, 117(10) (2015).
[5] Fayyad, U. M., Smyth, P., & R. Uthurusamy Piatetsky-Shapiro, G. : Advances in knowledge discovery and data mining (Vol. 21). (Eds.). Menlo Park: AAAI press. (1996)
[6] Govindarajan, M., & Srinivasan, B. : Ensembles of classification methods for data mining applications. International Journal of Information Engineering and Electronic Business, 5(6), 6-21. (2013).
[7] Kasem, M., & Hassanein, E. E. : Cloud business intelligence survey. International Journal of Computer Applications, 90(1) (2014).
[8] Mariscal, G., Marbán, Ó., & Fernández, C. : A survey of data mining and knowledge discovery process models and methodologies. The Knowledge Engineering Review, 25(2), 137-166. (2010).
[9] Pal, N. : Advanced techniques in knowledge discovery and data mining. Springer Science & Business Media. (2007).
[10] Pourshahid, A., Johari, I., Richards, G., Amyot, D., & Akhigbe, O. S. : A goal-oriented, business intelligence-supported decision-making methodology. Decision Analytics, 1(1), 1. (2014).
[11] Ranjan, J. : Business intelligence: Concepts, components, techniques and benefits. Journal of Theoretical and Applied Information Technology, 9(1), 60-70. (2009).
[12] Shearer, C. : The CRISP-DM model: the new blueprint for data mining. Journal of data warehousing, 5(4), 13-22. (2000)
[13] Tank, D. M. : Enable better and timelier decision-making using real-time business intelligence system. International Journal of Information Engineering and Electronic Business, 7(1), 43-48. (2015)
[14] Trkman, P., McCormack, K., De Oliveira, M. P. V., & Ladeira, M. B. : The impact of business analytics on supply chain performance. Decision Support Systems, 49(3), 318-327. (2010)
[15] Uçaktürk, A., Uçaktürk, T., & Yavuz, H. : Possibilities of Usage of Strategic Business Intelligence Systems Based on Databases in Agile Manufacturing. Procedia-Social and Behavioral Sciences, 207, 234-241. (2015)
[16] Wajong, A. M. R. : Business intelligent system to make management decision in project management. International Information Institute (Tokyo).Information, 18(8), 3353-3360. (2015)