Judul: Pengaruh Logam Berat Tembaga (Cu+2) Terhadap Pertumbuhan Miselia Cendawan Ektomikorhiza Cenococcum geophilum Pada Kondisi In Vitro
Abstrak: Dalam rangka merehabilitasi lahan kritis maka diperlukan bibit yang berkualitas, sehat, bagus, dan bermikorhiza. Pembibitan di lahan kritis memerlukan adaptasi dari setiap bibit yang ditanam terhadap tanah atau kondisi lapangan yang bervariasi seperti pada lahan alang-alang dan area bekas penambangan. Pada umumnya tanah bekas penambangan banyak mengandung unsur tembaga (Cu).
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Judul: Pengaruh dolomit, seng dan tembaga terhadp pertumbuhan serta serapan Zn dan Cu jagung pada latosol Darmaga
Abstrak: Tujuan dari penelitian ini adalah untuk mempelajari pemupukan Zn dan Cu terhadap pertumbuhan dan serapan kedua unsur tersebut pada jagung yang ditanam pada Latosol Penelitian dilaksanakan di rumah kaca Jurusan Tanah dan dilanjutkan dengan analisa laboratorium di Laborato- rium Jurusan Tanah, IPB dan Laboratorium Agronomi, Balai Penelitian Tanaman Pangan, Cimanggu, Bogor. Tanah yang digunakan dalam penelitian ini adalah Latosol Dramaga, berasal dari areal dengan vegetasi ka- ret yang diambil pada kedalaman O - 30 cm. Dosis pupuk yang digunakan adalah 200 ppm N, 150 ppm P, 150 ppm K, 1 ppm B dan 1 ppm Mo, masing-masing diberikan sebagai pupuk dasar dalam bentuk Urea, TSP, K2SO4, H3BO3 dan O dan (NH4) 6M07024.4H20. Pupuk ZnSO4.7H2O, CuSO4.5H20 dolomit diberikan sebagai perlakuan, masing-masing dengan dosis 0, 5, 10 dan 20 ppm Zn; 0,4, 8, dan 16 ppm Cu ser- ta O dan 5 ton/ha. Penelitian ini menggunakan Rancangan Acak Lengkap Faktorial. Parameter yang diamati adalah tinggi tanaman umur 45 hari, berat kering, serapan dan kadar Zn serta serapan dan kadar Cu tanaman bagian atas. Hasil penelitian menunjukkan bahwa dolomit memberi pengaruh nyata terhadap semua parameter yang diamati, kecuali kadar Zn tanaman bagian atas. Pengaruh nyata ini menunjukkan bahwa pemberian dolomit mempengaruhi kimia tanah, sehingga pertumbuhan tanaman menjadi lebih baik. Seng nyata meningkatkan tinggi tanaman dan serapan Zn tanaman bagian atas. Peningkatan tinggi tanaman berhubungan dengan peranan Zn dalam pembentukan auksin dan sistim enzim lain. Pengaruh Zn terhadap serapan dan kadar Zn tanaman disebabkan oleh meningkatnya ketersediaan Zn tanah. Berat kering, serapan dan kadar Cu tanaman bagian atas tidak menunjukkan respon nyata terhadap pemberian Zn. Pupuk ZnSO4 yang digunakan sebagai sumber Zn diduga belum mampu menekan serapan Cu, karena kontribusi ZnSO4 yang rendah terhadap Zn total larutan tanah. Peningkatan konsentrasi Zn akibat pemberian Zn tidak berpengaruh nyata terhadap konsentrasi Cu tanaman…dst
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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