Business Intelligence for Unemployment Rate Management System

 Tika Octri Dieni (University of Sriwijaya, Palembang, Indonesia)
 (*)Ken Ditha Tania Mail (University of Sriwijaya, Palembang, Indonesia)
 Fathoni Fathoni (University of Sriwijaya, Palembang, Indonesia)
 M Ihsan Jambak (University of Sriwijaya, Palembang, Indonesia)
 Pacu Putra (University of Sriwijaya, Palembang, Indonesia)

(*) Corresponding Author

Submitted: July 7, 2021; Published: August 1, 2021

Abstract

Unemployment rate is one of many problems which being faced by the government for each country, especially in Indonesia. Based on statistics data, Central Bureau of Statistics (Indonesia), unemployment rate in Indonesia is quite high which scattered throughout the province. Those data was obtained with a long proccess, by the time, human resources and cost which are not small. Thus, this paper proposes business intelligence for unemployment rate management system to predict from its several causes which are potentially on increasing unemployment rate, using Business Intelligence Roadmap methods, it used because its adaptive and detail which consists of 18 stages from 6 phases. There is one of the 18 stages, namely data mining, for this data mining using the KNN algorithm. Business intelligence can proccessing data into useful information or knowledge. Generally, business intelligence has main process which are data collection, then this data would be processed by ETL (Extract, Transform, Load) before get into data warehouse as a place data storage, so that those data could functionally used for analysis process with OLAP and data mining to classify the result of unemployment rate prediction from its several potentially causes. This paper would possibly to find patterns in the unemployment rate and its causes then the result of the pattern will visualizations on web application with business intelligence based that would be developed that easy to used / user friendly and attractive user interface

Keywords


Unemployment Rate; Business Intelligence; Business Intelligence Roadmap; KNN Algorithm; ETL; OLAP

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References

M. O. Nketia, “THE INTERNATIONAL JOURNAL OF BUSINESS & MANAGEMENT Business Intelligence ( BI ); A System to Optimize Business Performance,” vol. 4, no. 9, pp. 218–224, 2016.

I. Wowczko, “Business Intelligence in Government Driven Environment,” Int. J. Infonomics, vol. 9, no. 1, pp. 1107–1111, 2016.

M. Esteves, A. Abelha, and J. Machado, “The development of a pervasive Web application to alert patients based on business intelligence clinical indicators: a case study in a health institution,” Wirel. Networks, vol. 6, 2019.

Adnan, A. A. Ilham, and S. Usman, “Performance analysis of extract, transform, load (ETL) in apache Hadoop atop NAS storage using ISCSI,” Proc. 2017 4th Int. Conf. Comput. Appl. Inf. Process. Technol. CAIPT 2017, vol. 2018-Janua, pp. 1–5, 2018.

A. A. Yulianto and Y. Kasahara, “Implementation of Business Intelligence with Improved Data-Driven Decision-Making Approach,” Proc. - 2018 7th Int. Congr. Adv. Appl. Informatics, IIAI-AAI 2018, pp. 966–967, 2018.

R. Gaardboe, T. Nyvang, and N. Sandalgaard, “Business Intelligence Success applied to Healthcare Information Systems,” Procedia Comput. Sci., vol. 121, pp. 483–490, 2017.

S. S. Ally and N. Khan, “Data warehouse and BI to catalize information use in health sector for decision making: A case study,” Proc. - 2016 Int. Conf. Comput. Sci. Comput. Intell. CSCI 2016, pp. 92–97, 2017.

L. Gastaldi et al., “Measuring the maturity of business intelligence in healthcare: Supporting the development of a roadmap toward precision medicine within ISMETT hospital,” Technol. Forecast. Soc. Change, vol. 128, no. November 2016, pp. 84–103, 2018.

S. Oliveira, M. Esteves, R. Cernadas, A. Abelha, and J. Machado, “The development of a business intelligence web application to support the decision-making process regarding absenteeism in the workplace,” Adv. Intell. Syst. Comput., vol. 1137 AISC, pp. 104–113, 2020.

D. Prayitno, “Application of Business Intelligence for Banking Performance Based on Products Analysis,” Int. J. Progress. Sci. Technol., vol. 6, no. 2, pp. 554–569, 2018.

B. Tuccaroglu and M. Nat, “The readiness of banks for the application of Business Intelligence solutions,” 13th HONET-ICT Int. Symp. Smart MicroGrids Sustain. Energy Sources Enabled by Photonics IoT Sensors, HONET-ICT 2016, no. 2, pp. 127–132, 2016.

Y. Jadi and L. Jie, “An implementation framework of business intelligence in e-government systems for developing countries: Case study: Morocco e-government system,” Int. Conf. Inf. Soc. i-Society 2017, vol. 2018-Janua, pp. 138–142, 2018.

B. Oumkaltoum, E. I. Mohammed, E. B. M. Mahmoud, and E. B. Omar, “Business intelligence and EDA based architecture for interoperability of E-Government data services,” 5th IEEE Int. Smart Cities Conf. ISC2 2019, pp. 402–407, 2019.

A. Sorour, A. S. Atkins, C. F. Stanier, and F. D. Alharbi, “The role of business intelligence and analytics in higher education quality: A proposed architecture,” 2019 Int. Conf. Adv. Emerg. Comput. Technol. AECT 2019, 2020.

A. S. Girsang, D. A. Sunarna, A. Syaikhoni, and A. Ariyadi, “Business Intelligence for Education Management System,” 2019 Int. Conf. Comput. Sci. Inf. Technol. ICoSNIKOM 2019, 2019.

J. Fowler, “Business intelligence at the university,” Proc. - 6th Annu. Conf. Comput. Sci. Comput. Intell. CSCI 2019, pp. 821–825, 2019.

S. S. Wiradarma, K. D. Tania, and D. Y. Hardiyanti, “Implementation of Business Intelligence in Product Services a Banking (Case Study: PT Bank Sumsel Babel Baturaja Branch),” JSI J. Sist. Inf., vol. 9, no. 2, pp. 1242–1247, 2017.

H. Y. Riskiawan, T. D. Puspitasari, F. I. Hasanah, N. D. Wahyono, and M. Fatoni Kurnianto, “Identifying Cocoa ripeness using K-Nearest Neighbor (KNN) Method,” Proc. - 2018 Int. Conf. Appl. Sci. Technol. iCAST 2018, pp. 354–357, 2018.

S. Wang, R. Ma, Y. Li, and Q. Wang, “A bluetooth location method based on kNN algorithm,” ICENCO 2019 - 2019 15th Int. Comput. Eng. Conf. Util. Mach. Intell. a Better World, pp. 83–86, 2019.

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Copyright (c) 2021 Tika Octri Dieni, Ken Ditha Tania, Fathoni Fathoni, M Ihsan Jambak, Pacu Putra

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