Judul: Distribution of glyphosate herbicide and its effects on soil properties and plant growth
Abstrak: Herbisida glifosat di dalam tanah penting untuk diteliti keberadaannya, karena dapat memberikan kontribusi terhadap pengetahuan tentang nasib (fate) herbisida di dalam tanah. Tujuan penelitian ini ialah: (1) Mengetahui pola sebaran herbisida glifosat yang terinflitrasi ke dalam tanah; (ii) Mengetahui pengaruh residu herbisida glifosat terhadap sifat tanah dan pertumbuhan tanaman.
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Judul: Karakteristik Tanah Regosol Dan Latosol Darmaga Serta Dinamika Konsentrasi Residu Herbisida Glifosat Didalam Tanah
Abstrak: Penggunaan herbisida telah umwn dilalcukan petani dalam upaya menjaga usaha taninya agar berproduksi sesu.ai dengan yang diharapkan. Disisi lain, herbisida dapat merugikan karena dapat rnerusak ling'..cungan. Untuk mengetabui hal tersebut perlu dikaji perilaku herbisida di dalam tanah. Tanah Regosol dan Latosol diambil .sebagai contoh kasus masing-masing untuk tanah bertekstur dominan liat dan tanah bertekstur dom.inan pasir. Penelitian ini bertujuan untuk mengetahui karakteristik tanah Regosol dan Latosol, mengklasifikasikannya kedalam sistem Taksonomi Tamlh sampai kategori family serta mengetahui dinamika herbisida berbahan aktif glifosat pada beberapa kedalaman tanah, dan melihat perilaku glifosat di dalam tanah dengan tekstur berbeda dan mengkaitkannya dengan karakteristik tanah tersebut. Penelitian ini terdiri atas dua bagian. yaitu penelitian karakteristik dan klasifikasi tanah, serta peneHtian dinamika konsentrasi residu herbisida dalain kolom tanah. Bahan tanah Latosol berasal dari areal Kebun Percobaan Eakultas Pertanian, IPB di Darmaga dan memiliki tujuh susunan horison, sedangkan bahan tanah Regosol berasal dari Kebun Per\;obaan Fakultas Kehutanan, IPB Ji Dannaga dan memiliki empat susunan horison. Dalam penelitian dinamika konsentusi residu glifosat, bahan tanah Regosol yang digwiakan adalah ho:ison kedua. Sementara, bahan tanab Latosol yang digunakan adalah horlson ketiga, keempat clan kelimr. yang dicampur secara komposit. Penelitian dinamika konsentrasi residu glifosat ini terbagi menjadi dua tahap, yaitu percobaan pendahuluan dan percobaan utama. Percobaan pendahuluan dilakukan dengan male.sud menentukan dosis yang efektif untuk percobaan utama. Percobaan pendahuluan menggwiakan tiga dosis perlakuan sebesar .5 g/kolom tanah, 1.0 g/kolom tanah, dan l 00 g/kolom tanah. Sedangkan pada percobaan utama digunakan dosis perlakuan glifosat sebesar 4 glkolom tanah, 8 g/kolom tan.ah, dan 15 g/kolom tanah. Konsentrasi glifosat di dalam kolom tanah diukur pada kedalaman kolom 1 (0-5 cm), 2 (5-15 cm), dan 3 (15-45 cm)....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