Judul: Identifikasi Hama Ulat Kubis Menggunakan Transformasi Wavelet dengan Klasifikasi Probabilistic Neural Network
Abstrak: This research proposes a method for moth pests identification using wavelet transformation and Probabilistic Neural Netwok (PNN). Wavelet transformation method is used to get image features. This study focused on 3 types of moth pests that Crocidolumia binotalis zeller, Spodoptera exigua (Hubner), and Spodoptera litura F. To get the best performance of classifier was analyzed by confussion matrix and k-fold cross validation with k = 5. Result for identification of moth pests diamondback in this study showed an average accurancy of 80.74%. This result shows that the wavelet transformation and classifier Probabilistic Neural Network can be applied for the identification of cabbage caterpillar pests
Keyword: Wavelet Transformation., PNN, Cabbage Caterpillar Pest

Judul: Techniques of Texture Analysis for Identification of Crocidolomia pavonana, Spodoptera exigua, Spodoptera litura
Abstrak: Cabbage and green onion are two common vegetables cultivated by farmers in highland. Among major pests causing significant damage are Crocidolomia pavonana on cabbage and Spodoptera exigua on green onion. In addition, the generalist Spodoptera litura occasionally found attacking these vegetables. For unexperienced farmers as well as extension agents, the three caterpillars are not easily differentiated; although an accurate identification is required for a better control of the pests. Therefore, research was conducted with the objectives to develop identification system of the caterpillars based on computer vision technology. The three species of caterpillars have a unique texture and distinct feature from each other; and therefore, differentiating the species can be based on techniques of texture analysis. For this purpose, we used combination of several texture analysis: entropy feature, GLCM, Haralick features and Tamura feature. Each species consisted of 45 images, and consequently the total images used in this research were 135 images. Data work splitted using k-fold cross validation technique into training data (80%) and testing data (20%). Probabilistic Neural Network (PNN) was applied for classifying the three species of caterpillars. Research was conducted using GLCM with 1 pixel distance and 8 angles; Haralick features consisting of entropy, homogeneity, information of correlation 1, information of correlation 2; and Tamura feature consisting of coarseness; and entropy feature techniques. Our research revealed that Tamura features and information of correlation 1 were able to differentiate S. litura from other species. Entropy feature was able to differentiate C. pavonana with S. litura. Haralick entropy was able to differentiate C. pavonana with S. Exigua. Homogeneity was able to differentiate S. Exigua with S. litura whereas information of correlation 2 was not able to differentiate among the three species. However, combination of four techniques were able to improve the accuracy of caterpillar identification system. System performance obtained from the average accuracy of the entire fold reached 77.03%. The best accuracy (88.89%) was obtained when k = 5. Furthermore, we tested the system using classification model based on the fifth fold. Error occurred when C. pavonana incorrectly identified as S. exigua, and S. exigua incorrectly identified as S. litura. The error in identification were caused by inappropriate cropping of caterpillar images, the blur of image, and the caterpillar was not in advance stage. However, in general the pest identification system developed through this research can be used by farmers and extension agents who have difficulties in differentiating the three species of caterpillars.
Keyword: textures analysis, computer vision, pest identification

Judul: Konsumsi Minuman Beroksigen dan Dampaknya terhadap Performa saat Berolahraga, Profil Lipid, Glukosa Darah dan SGOT/SGPT
Abstrak: Exercise requires more energy (ATP) than normal activities. Oxygen consumption increases, while lung capacity is limited. It causes anaerobic respiration which produces lactic acid that reduces exercise performance. The purpose of this study was to assess the effects of short-term and long-term drinking of oxygenated water on the exercise performance and oxygen saturation (SpO2), lipid profile (cholesterol, triglycerides, LDL, and HDL) and blood glucose, as well as an influence on liver (AST/ALT). This study included (1) ethical clearance submission, (2) selection of potential respondents, (3) socialization and informed consent, (4) short-term experiment, (5) long-term trials, and (6) data processing. Recruitment of respondents was based on inclusion and exclusion criteria and medical check up. In the socialization, the right way of drinking samples was introduced. In the short-term and after long term experiments, twelve male student volunteers drank oxygenated water (50, 80 and 130 ppm) and normal water, 15 minutes before treadmill (10 Kmh) in Somatokinetika Laboratory of Jakarta State University. SpO2 was measured before and after treadmill, and 0, 5, 10, 15 minutes after drinking. In the long-term, seventeen male student volunteers drank oxygenated water (100 ppm) for 21 days and lipid profile, blood glucose, and AST/ALT were analyzed. Before and after intervention, blood plasma was used to measure the parameters in the Laboratory Clinic of dr Katili. Cholesterol, triglycerides, and blood glucose analysis used enzymatic colorymetry method, while HDL analysis used enzymatic colorymetry and precipitation method. LDL was calculated by Friedewald equation and AST/ALT analysis used kinetic method. Oxygenated water had no effect on SpO2 and exercise performance. Interestingly the average of VO2 max and time to fatigue of those drank oxygenated water were higher than those drank normal water. VO2 max (mL/Kg/menit) increased from 45.87 (normal water) to48.89 (50 ppm), 48.03 (80 ppm), 47.40 (130 ppm), and 48.76 (long-term). Time to fatigue was be reached 10.34 minutes (normal water), 11.18 minutes (50 ppm), 11.27 menit (80 ppm), 130 ppm (10.93 minutes) and 11.60 minutes (long-term). Long-term effects also showed the improvements of cholesterol (168.12 to 114.53 mg/dL), LDL (103.94 to 54.41 mg/dL) and the ratio of cholesterol : HDL, however they were also influenced by diet and intensity of activity. HDL, triglycerides, and blood glucose was not affected by oxygenated water and had no harmful effect on the liver.
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