Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal International Journal of Artificial Intelligence Research
Mayce Novitalia
Institut Pemerintahan Dalam Negeri
Computer Science & IT Electrical & Electronics Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Detection of Data Duplication and Anomalies in the Population Registration System to Improve the Quality of Public Services Mayce Novitalia; Selvi Diana Meilinda; Imelda Hutasoit; Riswati Riswati; Hendayana Hendayana; Dwi Agus Sumarno
International Journal of Artificial Intelligence Research Vol 10, No 1 (2026)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v10i1.1722

Abstract

The consequences of data duplication and anomalies on the quality of public service in Indonesia's Civil Registration Information System (SIAK) are investigated in this study. The study analyses documented anomaly patterns and institutional reports to back up its qualitative descriptive methodology, which is based on semi structured interviews with fifteen civil registration officers from five different districts. Multiple identities, inconsistent demographic features, faulty NIK formats, duplicate geographic coordinates, unrecognised or inactive Population Identification Numbers (NIK), and duplicate addresses are among the most common data errors, according to the research. As an example, NIK entries with less than 16 digits or infants classified as married are examples of logical and format abnormalities, which constitute the majority of errors. Data update cycles that are too long, inadequate automated validation, poor inter agency synchronisation, and reliance on human data entry are the main causes of these difficulties. Not resolving these irregularities might result in failed verification, delayed social assistance delivery, and termination of BPJS Health membership, therefore their impact is substantial. According to the research, public service quality may be enhanced by moving away from reactive rectification and toward preventive data governance. This can be achieved through the use of artificial intelligence (AI) for validation, real time updates, stronger integration between agencies, increased officer capacity, and public knowledge of data updating.
Co-Authors Dwi Agus Sumarno Hendayana Hendayana Imelda Hutasoit Riswati Riswati Selvi Diana Meilinda