Judul: Aeromonas sebagai penyebab septicemia pada ikan
Abstrak: Salah satu hambatan dalam usaha peningkatan produksi budidaya ikan adalah masalah penyakit. Adanya wabah penyakit ikan dapat mendatangkan kerugian ekonomi yang tidak sedikit. Dua spesies bakteri dari genus Aeromonas yang umum menyebabkan penyakit pada ikan adalah Aeromonas hydrophila dan Aeromonas salmonicida.
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Judul: Pola kepekaan Aeromonas Hydrophila terhadap beberapa antibiotika
Abstrak: Kasus Penyakit bakterial yang disebabkan oleh Aeromonas hydrophila pada ikan budidaya air tawar di Indonesia sampai saat ini masih merupakan salah satu bentuk kendala yang sangat merugikan. Pemakaian antibiotika untuk pengobatan penyakit bakte- rial pada ikan di Indonesia terutama dilakukan sejak terja- dinya wabah penyakit bakterial pada usaha budidaya ikan mas sekitar tahun 1980. Sedangkan cara pemakaian antibiotika untuk penanggulangan penyakit bakterial pada ikan dapat dila- kukan melalui makanan, perendaman dan suntikan. Penanggulangan penyakit bakterial pada ikan sampai saat ini masih bersifat mencoba-coba, antara lain dengan penggunaan chloramphenicol, oxytetracyclin dan streptomycin dengan dosis 10 - 50 ppm dalam waktu yang tidak ditentukan (Bullock et al., 1971). Beberapa jenis kemoterapi lain yang pernah digunakan dalam penanggulangan wabah Aeromonas hydrophila antara lain Nifuran, Malachite Green oxalate, kalium permanganat dan sulfamerazin. Penelitian ini bertujuan untuk mengetahui pola kepekaan Aeromonas hydrophila terhadap berbagai antibiotika secara in vitro dengan penggunaan Uji Antibiogram dan Uji Turbiditas. Rancangan penelitian yang digunakan adalah Rancangan Acak Lengkap dan Uji Banding Ortogonal untuk Uji Antibiogram. Hasil penelitian menunjukkan bahwa pemakaian antibiotika untuk menghambat dan menahan pertumbuhan Aeromonas hydrophila secara in vitro memberikan pengaruh yang sangat nyata (p<0.01), dengan Uji Banding Ortogonal dapat diketahui urutan kekuatan kerja dan efektivitas dari masing-masing antibiotika sebagai berikut: chloramphenicol, trimethoprim, erythromy- cin, neomycin, streptomycin atau kanamycin, tetracyclin dan novobiocin. Hasil Uji Turbiditas menunjukkan chloramphenicol pada kadar 5 ppm telah sangat efektif untuk menahan pertumbuhan Aeromonas hydrophila. Kanamycin mulai bekerja pada kadar 5 ppm dan benar-benar efektif pada kadar 30 ppm. Streptomycin mulai bekerja pada kadar 40 ppm dan benar-benar efektif pada kadar 50 ppm. Tetracyclin sama sekali tidak berfungsi sampai dengan kadar 50 ppm dalam menghambat pertumbuhan Aeromonas hydrophila secara in vitro pada suhu kamar…
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Judul: Development of Face Recognition Model using Bi-2DPCA and Support Vector Machine
Abstrak: The human face is always used in the application of access control systems, identity authentication systems, surveillance systems and security systems. Some researh on face recognition has been proposed with a variety of methods, such as Eigen Faces, Fisher’s Linear Discriminant, Bayesian Networks, Neural Networks,Hidden Markov Model, and so on. But all the above mentioned methods have limitations, such as dealing with the size of the image and lighting and learning time are too long. In a study to obtain an alternative method for face recognition, Le TH and Bui L (2011) conducted research model of face recognition with Two- DimensionalPrincipal Component Analysis (2DPCA) and Support Vector Machine (SVM) using AT&T face image data. The results of these studies that the accuracy of face recognition use 2DPCA and SVM was 97.3% and PCA-SVM was 95.2%. Then in 2010, Bi-2DPCA uses as alternative method of face recognition performed by Yang J et al. (2010). In a study comparing methods of Bi-2DPCA, 2DPCA and PCA. The conclusion that the use of Bi-2DPCA generating and computational accuracy was better than 2DPCA and PCA Based on research conducted by Le TH and Bui L (2011) and Yang J et al. (2010), conducted research for the development of models of face recognition using dimension reduction technique Bi-2DPCA and Support Vector Machine (SVM) as pattern recognition. 1DPCA and 2DPCA dimension reduction technique are used as comparison measure dimensions and computing time than using Bi-2DPCA technique. To help SVM in recognizing human faces, SVM use linear, polynomial and radial basis function (RBF) kernel function. When using RBF kernel, used for the RBF kernel, the parameter for sigma is 2-15 up to 23 and the value of C starting from 2-5 up to 214 which have been used in a study conducted by Le TH and Bui L (2011). Facial image data used are taken from the ORL Face Database AT&T Laboratory, which contain 40 faces, each face with 10 images, and total of image 400 pieces. Validation is done by Leave-One-Out (LOO) cross validation, so that for each pair of extraction and classifier method is 10 trials. One against all method used to help SVM in classifying the ORL face data consists of 40 classes, to reduce the computational time. From the results of tests on 36 models of face recognition , Bi-2DPCA85% with the help of a linear kernel generates the highest level of face recognition accuracy of 94.25 %, and the lowest accuracy rate of 24.50 % was obtained with 1DPCA-kernel polynomial ordo 3 . While the lowest computational time of 15.34 seconds when component is used by 85 % computation time and a maximum of 252.68 seconds when component is 95 %. There are several things that could be concluded that: pattern accuracy on each kernel feature extraction are the same. This means that there is no relationship between the feature extraction used by the kernel to the SVM. The second is, in this study SVM cannot classify large dimension data. From the results of tests on 36 models of face recognition, Bi2DPCA85% with the help
Keyword: Face Recognition, Principle Component Analysis, Kernel, Support Vector Machine, Leave-one Out Cross Validation