MAKING DASHBOARD BASED ON DATA MART USING POWER BI (CASE STUDY : UISI ADMISSION PART)
2. MATERIALS AND METHODOLOGY 1. Data Warehouse
2.5. Methodology
Fig. 4. Flow Chart of Research
3. RESULT AND DISCUSSION
In designing data mart based dashboards using Power BI for student subjects consisting of applicant prospective student data, prospective student data is accepted, and data on prospective student re-registration, student demographic data at UISI, as well as data from student schools in UISI.
So that the data mart scheme that is designed consists of demographic data mart scheme for
prospective student applicants, data mart scheme for demographics of prospective students accepted, data mart scheme demographics of prospective students to re-register, schema data mart UISI student demographics, and data mart schemes for students from UISI. The data mart scheme is composed of dimension tables and fact tables, along with a data mart scheme that has been created.
Fig. 5. Data mart scheme demographics of student registrants Information that can be obtained from the
data mart scheme in Figure 5 is the year of entry of prospective students, the province of origin of
prospective students, the district from the prospective student, and the number of prospective student registrants in UISI from various cities in Indonesia.
Fig. 6. Student demographic data mart scheme is accepted
ICMST 2019 August, 1 2019 Information that can be obtained from the data
mart scheme in Figure 6 is the year of entry of prospective students, the province of origin of prospective students, the district of the prospective
student, and the number of prospective students who have been accepted in UISI from various cities in Indonesia.
Fig. 7. Student demographic data mart scheme re-registers Information that can be obtained from the
data mart scheme in Figure 7 is the year of entry of prospective students, the province of origin of prospective students, the district of the prospective
student, and the number of prospective students who have re-registered in UISI from various cities in Indonesia.
Fig. 8. Data mart scheme for UISI student demographics The information that can be obtained from
the data mart scheme in Figure 8 is the year of entry of prospective students, the province of origin of prospective students, the district of prospective
student candidates, study programs in UISI and the number of students based on study programs in UISI.
Fig. 9. Data mart scheme for the school of origin of UISI students Information that can be obtained from the
data mart scheme in Figure 9 is the year of entry of prospective students, the province of origin of prospective students, the district of origin of prospective students, the school of origin of students and the number of students based on the school of
origin of the student. After the scheme of the required data mart has been made properly and correctly, then next is to make a visualization of the existing data mart scheme into a dashboard display using Power BI, as follows.
Fig. 10. Dashboard for demographics of registrant students In Figure 10 displays a dashboard about the
demographics of applicant students spread across various provinces and districts in Indonesia, there is a year and province filter on the left to display the
number of students based on the filter selected. To see the details of the number of students, just point the pointer at the existing pie chart.
ICMST 2019 August, 1 2019
Fig. 11. The dashboard for student demographics is accepted In Figure 11 shows a dashboard about the
demographics of students accepted spread across various provinces and districts in Indonesia, there is a year and province filter on the left to display the
number of students based on the filter selected. To see the details of the number of students, just point the pointer at the existing pie chart.
Fig. 12. The dashboard for student demographics is re-register In Figure 12 displays a dashboard about the
demographics of students who re-register scattered in various provinces and districts in Indonesia, there is a year and province filter on the left to display the
number of students based on the filter selected. To see the details of the number of students, just point the pointer at the existing pie chart.
Fig. 13. Dashboard for UISI student demographics In Figure 13 displays a dashboard about the
demographics of UISI students per study program spread across various provinces and districts in Indonesia, there is a year filter and study program
on the left to display the number of students based on the filter selected. To see the details of the number of students, just point the pointer at the existing pie chart.
Fig. 14. School dashboard from UISI students
In Figure 14 shows a dashboard about schools from UISI students spread across various provinces and districts in Indonesia, there is a year and province filter on the left to display the number of students based on the filter selected. To see the details of the number of students, just point the pointer to the existing bar diagram.
4. CONCLUSION
Presentation of data and information from student data refers to the OLTP data source owned by the UISI admission. The data source becomes a reference in building staging data, data mart and student demographic dashboards. The collection of data contains information that can be analyzed, so
ICMST 2019 August, 1 2019 that a knowledge can be used that can be used for
universities in conducting promotional processes through BI Application in the form of dashboards.
The results of the data that have been processed in the data mart, produce a fact table that is displayed in the form of a dashboard visualization with an Indonesian map model. The dashboard that has been created can present an information on demographic data of UISI students accurately based on provinces and districts throughout Indonesia.
Staging data, data mart, and dashboards that have been made are still around student demographics. In the future, it is necessary to build a data staging, data mart, and dashboard in other fields, so that marketing and admissions can explore and analyze even more information. In the process of developing the next dashboard, the pie chart is displayed as a percentage.
REFERENCES
Ahmad, F. (2016) ‘Marketing Management in Increasing Student Reception at MI Darul Hikmah Bantarsoka, Purwokerto Barat District, Banyumas Regency.
Asroni (2014) ‘Data Warehouse Design of the Executive Information System Using the Kimball Method (Case at Muhammadiyah University, Magelang)’, p. 308992.
Golfarelli, M. and Rizzi, S. (2009) Data warehouse design: Modern principles and methodologies. McGraw-Hill New York.
Imelda (2008) 'Business Intelligence', Business Intelligence, 11 (Business Intelligence), pp.
111–122.
Imhoff, C., Galemmo, N. and Geiger, J. G. (2003) Mastering Data Warehousing Design:
Relational and Dimensional Techniques.
Kimball, R. and Ross, M. (2011) The data warehouse toolkit: the complete guide to dimensional modeling. John Wiley & Sons.
Kurniawan, N. B. (2011) ‘Data Warehouse Design and Implementation Case Study of Mapping Disaster Prone Areas in West Sumatra Province’. Bandung.
Muhammad, M. S. (2017) 'Hope Of Stakeholders In Islamic Education Institutions', 14 (2). Oracle (2002) Oracle9i Data Warehousing Guide, Release 2, A96520-01.
Paramita P., D., Firdaus and Afrina, M. (2012)
‘Application of XYZ Hypermarket Sales Data Mart Using the Method From Enterprise Models To Dimensional Models 1,2,3 ', Journal of Information Systems, 4 (2), pp.
503-515.
Rusdi, H. (2017) Making Data Mart for Power BI- based Executive Dashboard (Case Study:
Personnel Data Institute of Technology, November 10). The Ten November Institute of Technology.
UISI (2016) Profile of UISI - Semen Indonesia International University (UISI), UISI. Available at: https://uisi.ac.id/read/profil-uisi (Accessed:
2 February 2018).