TY - GEN
T1 - The Importance of Data Quality to Reinforce COVID-19 Vaccination Scheduling System
T2 - 2nd International Conference on Information Technology and Education, ICIT and E 2022
AU - Siregar, Dwi Yanti
AU - Akbar, Hidayat
AU - Pranidhana, Ida Bagus Putu Angga
AU - Hidayanto, Ahmad Nizar
AU - Ruldeviyani, Yova
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Vaccination is one of the solutions to reduce the spread of COVID-19. Jakarta Government collaborates with the Department of Population and Civil Registration and the Provincial Health Office to build a COVID-19 vaccination scheduling system to reinforce the vaccination process in Jakarta. The development process involves 3 major stakeholders, so it requires very intense coordination and data exchange. Department of population and civil registration provides population data as vaccination targets. This data has been integrated into the system of the Jakarta Government. However, some other data, such as the location and quota of vaccination from the Provincial Health Office is collected manually using a spreadsheet. Manual exchanging data tends to cause data is often inaccurate, incomplete, inconsistent, and duplicate. This study aims to measure data quality (DQ) of the COVID-19 vaccination scheduling system in Jakarta. This study uses Total Data Quality Management (TDQM). TDQM provides a common framework to facilitate understanding in data improvisation through data quality management. Measurement and analysis of the data on database of the system using a tool, Talend. The measurement discovers that completeness (null 60.80% and blank 21.36%), validity 92.18%, accuracy 99.11%, and uniqueness 99.38%. The result shows that some data were poor quality especially due to incomplete data.
AB - Vaccination is one of the solutions to reduce the spread of COVID-19. Jakarta Government collaborates with the Department of Population and Civil Registration and the Provincial Health Office to build a COVID-19 vaccination scheduling system to reinforce the vaccination process in Jakarta. The development process involves 3 major stakeholders, so it requires very intense coordination and data exchange. Department of population and civil registration provides population data as vaccination targets. This data has been integrated into the system of the Jakarta Government. However, some other data, such as the location and quota of vaccination from the Provincial Health Office is collected manually using a spreadsheet. Manual exchanging data tends to cause data is often inaccurate, incomplete, inconsistent, and duplicate. This study aims to measure data quality (DQ) of the COVID-19 vaccination scheduling system in Jakarta. This study uses Total Data Quality Management (TDQM). TDQM provides a common framework to facilitate understanding in data improvisation through data quality management. Measurement and analysis of the data on database of the system using a tool, Talend. The measurement discovers that completeness (null 60.80% and blank 21.36%), validity 92.18%, accuracy 99.11%, and uniqueness 99.38%. The result shows that some data were poor quality especially due to incomplete data.
KW - COVID-19
KW - data quality
KW - Talend
KW - TDQM
KW - vaccination
UR - http://www.scopus.com/inward/record.url?scp=85129935553&partnerID=8YFLogxK
U2 - 10.1109/ICITE54466.2022.9759880
DO - 10.1109/ICITE54466.2022.9759880
M3 - Conference contribution
AN - SCOPUS:85129935553
T3 - Proceedings - 2022 2nd International Conference on Information Technology and Education, ICIT and E 2022
SP - 262
EP - 268
BT - Proceedings - 2022 2nd International Conference on Information Technology and Education, ICIT and E 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 22 January 2022
ER -
