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All Journal Jurnal Riset Informatika
Nasution, Surya Micrandi
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Computer Science & IT

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A SEASONAL IMPUTATION METHOD FOR ADDRESSING MISSING DATA IN ENVIRONMENTAL IOT SENSOR TIME SERIES Ramadhan, Ardiansyah; Nasution, Surya Micrandi; Septiawan, Reza Rendian; Trisnawan, I Kadek Nuary; Afinda, Angel Metanosa
Jurnal Riset Informatika Vol. 8 No. 2 (2026): Maret 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i2.475

Abstract

Missing and incomplete observations in Environmental IoT sensor networks reduce data reliability and disrupt analyses, especially for temperature and humidity time series exhibiting strong diurnal seasonality. This study develops and evaluates a seasonal imputation method to address missing data in IoT-based environmental monitoring, using a workflow of anomaly detection, outlier removal, time-of-day-aware imputation, and performance evaluation under varying missing-rate scenarios. Key challenges include sensor noise, connectivity issues, and intermittent hardware failures, which degrade data integrity and affect trend analysis, forecasting, and anomaly detection. To mitigate these, the method uses hourly and minute-level seasonal patterns after filtering out physically unrealistic values. Experimental results show high accuracy and robustness in reconstructing temperature and humidity data: temperature imputation achieves MAE values of approximately 0.86–0.87°C, and humidity yields MAE values of 3.92–4.01%RH, with no performance drop even at 50% data loss. The imputed series preserves natural diurnal dynamics without introducing distortions, effectively restoring continuity and structural consistency in environmental IoT time series for reliable modeling, feature extraction, and decision support.
Co-Authors Afinda, Angel Metanosa Ramadhan, Ardiansyah Reza Rendian Septiawan Trisnawan, I Kadek Nuary