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: Kinetika dan Termodinamika Korosi Logam Cu dan Inhibisinya oleh Zink (Disikloheksilditiofosfat) dengan Teknik Polarisasi Potensiodinamik
Abstrak: Zinc dialkyldithiophosphates (ZDTP) is a lubricant additives which has many functions such as anti-friction, anti-wear, antioxidants, and anti-corrosion. In this research, ZDTP type which was made is ZDTP with alkyl cyclohexyl group (ZDTPS). The yield of ZDTPS were 83.50% and 82.10%. Inhibition effectivity of zinc dicyclohexyldithiophosphate were 94.30% in concentration 3% and increase significantly in concentration 1%. The parameters of thermodynamics was measured to show the easiness of corrosion reaction occur. Themodynamics parameters of blank revealed ΔH∗ was 26.8583 kJ/mol, ΔS∗ was -150.4102 J/molK, and ΔG∗ was 72.4295 kJ/mol. Thermodynamics parameters of ZDTPS revealed ΔH∗ was 50.0563 kJ/mol, ΔS∗ was -79.9807 J/molK, and ΔG∗ was 74.2963 kJ/mol. Activation energy from the corrosion process with ZDTPS presence was 52.6509 kJ/mol. This value is bigger than energy activation of blank with the value was 29.4548 kJ/mol.
Keyword: Bogor Agricultural University (IPB), ZDTP, thermodynamic, polarization, kinetic, inhibition effectivity