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Nanoparticles have wide-scale applications in various areas, including medicine, chemistry, electronics, and energy generation. Several physical, biological, and chemical methods have been used for synthesis of silver nanoparticles. Green synthesis of silver nanoparticles using plants provide advantages over other methods as it is easy, efficient, and eco-friendly. Nanoparticles have been extensively studied as potential antimicrobials to target pathogenic and multidrug-resistant microorganisms. Their applications recently extended to development of antivirals to inhibit viral infections. In this study, we synthesized silver nanoparticles using Cinnamomum cassia (Cinnamon) and evaluated their activity against highly pathogenic avian influenza virus subtype H7N3. The synthesized nanoparticles were characterized using UVVis absorption spectroscopy, scanning electron microscopy, and Fourier transform infrared spectroscopy. Cinnamon bark extract and its nanoparticles were tested against H7N3 influenza A virus in Vero cells and the viability of cells was determined by tetrazolium dye (MTT) assay. The silver nanoparticles derived from Cinnamon extract enhanced the antiviral activity and were found to be effective in both treatments, when incubated with the virus prior to infection and introduced to cells after infection. In order to establish the safety profile, Cinnamon and its corresponding nanoparticles were tested for their cytotoxic effects in Vero cells. The tested concentrations of extract and nanoparticles (up to 500 μg/ml) were found non-toxic to Vero cells. The biosynthesized nanoparticles may, hence, be a promising approach to provide treatment against influenza virus infections.

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Flood is the most common disaster in Indonesia and certainly harmful to society in the form of material or psychical. Therefore, it's necessary to identify the potential and flood mitigation earlier to reduce the potential losses suffered by the society after the occurrence of disaster. This is difficult to do with conventional methods so that in this research proposed "Neural Network Learning Vector Quantization as Identification Method of Potential and Mitigation of Flood Disaster". With this algorithm specified four nodes input layer, one hiden layer with two neurons and two output layers where four nodes input layer are elevation, drainage, rainfall and flood events are derived from data of BPS Malang, BMKG Karangploso, and data of BPBN. Data processing and testing will generate two outputs, they are identification of flooding potential area and no flooding potential area in every villages in Malang. The test results by using confution matrix showed the accuracy value at 95.34%, sensitivity value at 100%, specification value at 95.29%, and error rate at 4.68% on 1710 dataset that composed of 70% training data and 30% testing data with learning rate at 0.1, decrement learning rate at 0.01, maximum epoch at 10 and minimum epoch at 0.0000001. I.