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


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


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

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

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