Judul: Pengembangan wahana pencitraan bawah air guna identifikasi dan kuantifikasi terumbu karang dengan metode jaringan syaraf tiruan
Abstrak: Initially finger printing methods was used by police for criminal case investigation; then expanded to other practical application. Artificial Neural Network is one of the technique to process data to identify and recognize a particular pattern similar to the way human brain functions. In this thesis, I investigate and identify various coral features using artificial neural network (ANN) back-propagation method. The underwater vehicle was constructed to collect coral imagery, using; stainless steel body with two floats on the left and right and equipped with camera, GPS-map sounder, and light sources. The 3000 images were collected and pre-processed, to extract descriptor or key parameters of the features, during field measurements. The ANN back-propagation applied to identify the life form as sub-massive coral has an accuracy of 92.86%. Therefore, the application of this technique for features identification of the coral life form is recommended..
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Judul: Identifikasi Citra Karang Menggunakan Jaringan Syaraf Tiruan: Kasus Family Pocilloporidae
Abstrak: Nowadays, the trend method for coral identification is by using the visual technique since it does not have to take the coral sample which can damage the coral growth. This technique needs visual skill and the ability to classify the patterns which are formed by the color, the texture and the shape of coral. So far, this visual technique can only be done by the expert and experienced researchers. Therefore, in this research, the implementation of backpropagation artificial neural network is done to identify the genus image of corals as the application of visual technique in computer system. Hopefully, this application can help and make it easier for the early-stage researchers to identify corals from the features of photography results. The genus which belong to the family of pocilloporidae. The feature extractions; colors,textures and shapes are used as the input to the network. The methods are RGB and HSV for color, moment invariant for shape and three methods which are statistical moment, gray-level co-occurrence matrix (GLCM) and local binary patterns (LBP). The experiments towards the combination of color and shape features on each texture feature, show that the identification level of color, texture (statistical moment) and shape is better than other feature combinations. The result of the recognition rate is 96% and the elapsed time ìs 10.94 second. The recognition rate of color, texture (GLCM) and shape is 96% and the elapsed time is 20.849 second. While the recognition rate on color, texture (LBP) and shape is lower, that is 68% and the elapsed time is 0.465 second.
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Judul: Pengaruh Konsentrasi Katalis Methyl Ester Sulfonic Acid Terhadap Sifat Fisikokimia Gliserol Ester Oleat yang Dihasilkan
Abstrak: Glycerol is a by-product from palm olein biodiesel industry which could be value-added to obtain various products. One of the alternatives is to modify glycerol become glycerol ester oleat. Before processing, glycerol was first refined using 5% (v/v) phosphoric acid 85%. The refinery showed that glycerol content was increased from 49% to 80%. Glycerol was then esterified with oleic acid and catalyst methyl ester sulfonic acid (MESA) to obtain glycerol ester. Esterification was conducted at 240°C and nitrogen atmosphere using 200 rpm of mixing for 90 minutes. The best catalyst concentration used in esterification process is at 0,5% and resulting glycerol ester oleat with acid number 48,07 mg KOH/g sample, density of 0,9124 g/cm3, viscosity 68,32 cP (25°C), kinematic viscosity 36 cSt (40°C), flash point 230°C and pour point -15°C.
Keyword: glycerol ester, glycerol