Judul: Pengelompokan citra tanda tangan menggunakan metode som kohonen dua dimensi dan praproses wavelet
Abstrak: Biometric is a science for recognizing the identity of a person based on their behavioral traits. A signature is one of the characteristics of human behavior. This research aims to cluster signature images using two dimension self organizing maps (SOM) Kohonen with and without wavelet transformation preprocessing. SOM Kohonen is a type of artificial neural network that has unsupervised learning method. The images used are 40x60 grayscale signature images. Image dimension is reduced to half of the image dimension using Haar Wavelet transform. Silhouette coefficient and Davies Bouldin Index (DBI) are used as the evaluation functions for the clustering. The result shows that optimum clustering for cluster images without Haar Wavelet transform is obtained when alpha is equal to 0.7, initial width neighbor equal to 4 and final width neighbor equal to 4. Optimum clustering for cluster images using Haar Wavelet transform is obtained when the value of DBI is minimum, which is achieved when the value of alpha is equal to 0.8, initial width neighbor equal to 4 and final width neighbor equal to 2
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Judul: Signature Recognition using VFI5 Algorithm and Wavelet Preprocess.
Abstrak: Biometric is a science in recognizing the identity of a person based on their physical or behavioral traits. Today, one of the most widely applied biometric object is hand-written signature. In this research, we tried to identify offline handwritten signature using VFI5 (Voting Feature Intervals 5) classification algorithm. The VFI5 classification algorithm has been used previously on non-image objects with promising result. The feature used in the classification is the grayscale value of the image and the signature image dimension used in this research is 40×60 pixels which means originally there are 2400 features that relatively large to compute. Thus, Haar wavelet transform is used to reduce the dimension. This is done by taking only the approximation image of the transformation result, and leave the horizontal, vertical, diagonal details. The wavelet decomposition is applied until the fifth level, creating new low level images with 20×30, 10×15, 5×8, 3×4, 2×2 dimension respectively for the first, second, third, fourth, and fifth decomposition levels. The approximation images then classified using VFI5 algorithm, with 97.5%, 95%, 90%, 65%, and 62.5% accuracy respectively for the first, second, third, fourth, and fifth decomposition levels. It turned out that VFI5 algorithm can be used to identify images with wavelet transform to reduce the image dimension with good result, and the higher decomposition level applied, the more accuracy dropped. Keywords: signature identification, wavelet image dimension reduction, image classification, VFI5 algorithm.
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Judul: Methane Consumption (CH4) on Upland Rice Cultivation Under Micro Climate Cobdition With N Fertilizer Levels
Abstrak: Methane (CH4) is one of radiatively active trace gases or greenhouse gas, having strong absorbsivity to the infrared outgoing radiation emitted by the earth's surface, hence causing the warming of the atmosphere. Methane concentration in the atmosphere increases with a rate of about 12+1 ppbv/yr, or 36 Tg/yr. The increase of methane was predicted to have a significant impact on the earth's climate. The presence of 1.3 ppmv of methane in the atmosphere causes the globally average surface temperature 1.30C higher than it would be with no methane.
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