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Digital Transformation in Environmental Security Management in Calosa Cluster, Bandung Regency Surya Michrandi Nasution; Reza Rendian Septiawan; Ardiansyah Ramadhan
JARDIRA – Jurnal Pengabdian Digital dan Rekayasa Informatika Vol. 1, No. 1, July 2025
Publisher : CogniSpectra Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65917/jardira.v1i1.20

Abstract

Background: This community service activity aims to enhance environmental security in residential areas through digital transformation by integrating technology and community empowerment. Conducted at the Calosa Cluster, Bandung Regency, this program was initiated due to frequent losses and the limited effectiveness of existing surveillance systems, especially in blind spots. The objective is to develop a sustainable and community-managed security system using modern surveillance technology. Contribution: This program contributes to the community by increasing residents' sense of security, enhancing technological literacy, and fostering collective responsibility in managing neighborhood safety. Method: A participatory and collaborative approach was applied, involving residents, housing managers, and a university implementation team. The method included needs analysis, planning, training, and system installation. Surveys and interviews were conducted to identify security gaps, followed by the installation of devices and training on CCTV operation and data management. Results: The implementation successfully improved surveillance coverage and reliability. Residents gained practical skills to operate and manage the system independently. The positive response indicated a strengthened sense of security and community involvement. Conclusion: The program successfully addressed the security challenges in the Calosa Cluster through a digitally based, community-oriented solution. It demonstrates that integrating technology and community participation can create an effective, sustainable environmental security model applicable to similar residential areas.
A SEASONAL IMPUTATION METHOD FOR ADDRESSING MISSING DATA IN ENVIRONMENTAL IOT SENSOR TIME SERIES Ardiansyah Ramadhan; Surya Micrandi Nasution; Reza Rendian Septiawan; I Kadek Nuary Trisnawan; Angel Metanosa Afinda
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.