Judul: Identification Seagrass Condition from ALOS AVNIR-2 using Artificial Neural Network at Pari Island
Abstrak: Seagrass beds have important roles in marine life, but the unavailable of information about the condition of seagrass causes difficulties in managing coastal areas properly. Regularly updated and accurate information on the percentage cover of seagrass is an essential component of the knowledge required to monitor, understand and manage this resource. Artificial Neural Network (ANN) was applied to ALOS AVNIR-2 to identify seagrass condition. Twenty two classification scenarios were done to compare the result of accuracy. There are three class of seagrass condition. Seagrass cover more than 60% indicates the condition of good seagrass. Seagrass cover from 30% to 59,9% indicates the condition of medium seagrass. Seagrass cover below 29,9% indicates the condition of poor seagrass. Best accuracy was obtained by scenario H by entering combination of blue (0.42 to 0.50 μm) and NIR (0.76 to 0.89 μm) wavelength plus water depth data as input parameters, with a value of 71.43% overall accuracy. However, looking at individual class, Scenario C, which is 58.33% of overall accuracy by using combination of blue (0.42 to 0.50 μm), green (0.52 to 0.60 μm), NIR (0.76 to 0.89 μm) wavelength plus water depth achieved higher producer and user accuracy. Overall, the result of identification seagrass condition from ALOS AVNIR-2 using artificial neural network at Pari Island in 2010 is dominated by poor seagrass, while good seagrass and medium seagrass were found in small area.
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Judul: Mangrove Classification on ASTER VNIR using Vegetation Indices and Neural Networks Study Case : Berau Delta, East Kalimantan.
Abstrak: ASTER with its VNIR sensor have the capability for mapping vegetation. With the resolution of 15m x 15m, this image should be able to classify mangrove is more details than Landsat TM with resolution of 30m x 30m. Based on this, algorithms was applied to explore the capability of this image for classifying mangrove. These are Standard Back Propagation Neural Network and some vegetation indices (DVI, NDVI, SR, and the modified of these algorithm by using Natural Color Composite : NCC-DVI, NCC-NDVI, NCC-SR). While, as study area, Berau Delta, East Kalimantan is chosen because there are still may found many species of mangroves. Each algorithm show different results, but almost all of them capable to classify mangrove into zone level, except modification of vegetation index algorithms. Standard Back Propagation Neural Network is not quite good for classifying mangrove, because it can generate pixels into lower number of classes. While, DVI, NDVI, and SR can classify mangrove into 4 class. Its rather low than Maximum Likelihood Supervised classification and ISODATA Unsupervised classification which produced 5 class. But, when modified vegetation index applied, it a different results. They can detect Nypa fruticans class clearly than other species. Even, by using NCC-NDVI and NCC-SR class Nypa still can differentiate into detailer condition. The specific condition of the area of study where high frequency of rain, may cause the amount of water content in the vegetation become increase. Since gives influence in low value of NIR reflectance and high reflectance of RED channel, the modified algorithm of vegetation indices using NCC applied, and show result better than standard algorithm of vegetation indices.
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Judul: Katalis Zink Ferit Tersubstitusi Cu untuk Oksidasi Benzil Alkohol
Abstrak: Katalis berperan dalam menurunkan energi aktivasi. Katalis ZnFe2O4 dan ZnFe2O4 tersubstitusi Cu disintesis menggunakan metode kopresipitasi untuk digunakan dalam oksidasi benzil alkohol. Katalis dicirikan dengan spektrofotometer inframerah (FTIR) dan difraktometer sinar-X. Spektrum FTIR menunjukkan puncak serapan dari ikatan Zn-O dan Fe-O masing-masing pada rentang 559‒575 cm-1 dan 419‒447 cm-1. Difraktogram menunjukkan terbentuknya fase tunggal Zn0.6Cu0.4Fe2O4 pada katalis ZnFe2O4 tersubstitusi Cu 20%. Benzil alkohol dioksidasi dengan sistem refluks. Konversi benzil alkohol meningkat dengan meningkatnya suhu. Kadar benzaldehida yang dihasilkan dengan katalis ZnFe2O4 tersubstitusi Cu 20% lebih tinggi dibandingkan dengan katalis ZnFe2O4 yang tidak tersubstitusi dan yang tersubstitusi Cu 60 dan 80%. Hal tersebut menunjukkan bahwa katalis ZnFe2O4 tersubstitusi Cu 20% memiliki kinerja yang lebih baik dalam oksidasi benzil alkohol menjadi benzaldehida dengan kadar benzaldehida 8.763%.
Keyword: Benzil alkohol, katalis, oksidasi, ZnFe2O4