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All Journal Engineering, Mathematics and Computer Science Journal (EMACS)
Aufauzan, Brian Tirafi
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering Mathematics

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Journal : Engineering, Mathematics and Computer Science Journal (EMACS)

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Web-Based Quality Control Dashboard Design for Data Validation and Monitoring: A Case Study of BMKG Instruments Purwandari, Kartika; Aufauzan, Brian Tirafi; Sigalingging, Join Wan Chanlyn
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 8 No. 1 (2026): EMACS (In Press)
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v8i1.13532

Abstract

Accurate meteorological data are vital for the operational activities of the Agency for Meteorology, Climatology, and Geophysics (BMKG), specifically for weather forecasting and disaster mitigation. However, Automatic Weather Station (AWS) instruments frequently encounter sensor degradation and technical malfunctions, which compromise data validity. Traditional manual validation is inefficient and prone to human error. This study addresses these gaps by designing a web-based Quality Control (QC) dashboard for real-time AWS data monitoring. Developed using the Laravel framework and PostgreSQL, the system integrates Leaflet.js and Chart.js for interactive spatial and analytical visualization. Using the Agile Scrum methodology, the development process was iteratively refined across eight sprints. Implementation results show a significant improvement in data validation accuracy and a reduction in potential human error. User Acceptance Testing (UAT) with fifteen BMKG specialists confirms high usability, with the system receiving "Strongly Agree" ratings for its efficiency in real-time monitoring and reporting. The practical implications include enhanced data credibility for national climate modeling. This paper concludes that while the dashboard streamlines workflows, future iterations should incorporate automated anomaly detection algorithms. Limitations include a current reliance on static validation thresholds, suggesting a need for machine learning integration in future research.
Co-Authors Join wan chanlyn Sigalingging Purwandari, Kartika