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All Journal International Journal of Enterprise Modelling
Eliana Maharani
Universitas Pertahanan Republik Indonesia, Bogor, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Transportation

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A literature review: image restoration and enhancement techniques for slow scan television Eliana Maharani; Dananjaya Ariateja; Uvi Desi Fatmawati
International Journal of Enterprise Modelling Vol. 20 No. 2 (2026): May: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/int.jo.emod.v20i2.197

Abstract

This systematic literature study seeks to computationally develop the optimum picture restoration architecture to handle the dual analog-digital degradation (strip noise and motion blur) of Slow Scan Television (SSTV) transmissions in narrowband radio frequencies during emergency operations. The study critically assessed 28 peer-reviewed algorithmic frameworks published between 2007 and 2025 using PRISMA criteria. The data synthesis procedure used a comparative analysis matrix to evaluate algorithms' quantitative efficacy, including PSNR and SSIM, against specific analogue failure instances. Under compounding degradation, individual computational methods fail, as shown by the synthesis. When exposed to severe analogue noise, unguided generative models hallucinate, whereas conventional spatial filters degrade edges. Comparative empirical research shows that a cascaded hybrid framework works best. Using a 5x5 median filter with 1D directed filtering as pre-processing suppresses high-density impulsive anomalies, improving baseline PSNR by 2.4 dB. The kernel-guided diffusion model over the pre-cleaned matrix accurately reconstructs structural weaknesses, raising SSIM indices to 0.92 even in datasets with significant oscillator blur. A quantitatively validated, domain-specific restoration process that combines spatial denoising with advanced generative priors is the main contribution of this research. This study scientifically proves that kernel-based diffusion models need spatial variance pre-filtering to work in radio-degraded scenarios, providing a reliable emergency visual communication framework for authentic signal reconstruction in internet-deprived environments.
SIKELAS: A smart safety system for the elderly using AI and IoT technology Michael Faldo; De Sheperd Guella Winisia Zega; Hizkia Purba; Eliana Maharani
International Journal of Enterprise Modelling Vol. 20 No. 2 (2026): May: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/int.jo.emod.v20i2.199

Abstract

The number of elderly people in Indonesia continues to rise every year, posing serious challenges regarding their well-being and safety, particularly in nursing homes where caregiver shortages and limited monitoring capabilities remain critical issues. Existing elderly monitoring systems are typically limited to single-modality approaches, such as vision-only fall detection or single-sensor environmental monitoring, lacking real-time multimodal integration. To address these gaps, the “SIKELAS” (Smart Safety System for the Elderly) system was developed as a novel integrated solution combining AI technology, anomaly detection, voice recognition, and five environmental sensors into a single unified real-time monitoring platform. The novelty of SIKELAS lies in its simultaneous integration of MediaPipe-based fall and gesture detection, Speech-to-Text voice recognition, and multi-sensor environmental monitoring coordinated through an ESP32 microcontroller and Flask back-end, delivering automated Telegram alerts with an average response time under 1 second. This research employs an experimental quantitative approach with controlled laboratory testing across five datasets. Key results: gesture detection accuracy 98.38%, fall detection accuracy 89.16%, gas detection above 2500 threshold, flame detection below 500 threshold, and ultrasonic error below 3%. SIKELAS outperforms previous single-modality systems, delivering a comprehensive, measurable solution that reduces caregiver workload through automated multimodal monitoring and real-time emergency notifications.
Co-Authors Dananjaya Ariateja De Sheperd Guella Winisia Zega Hizkia Purba Michael Faldo Uvi Desi Fatmawati