Judul: Pengaruh Pemberian Zeolit terhadap Kapasitas Tukar Kation (KTK) Tanah dan Produksi Tomat (Lycopersicon esculentum Mill.)
Abstrak: Penelitian ini dilaksanakan di Rumah Kaca dan Laboratorium Jurusan Tanah, Fakultas Pertanian, Institut Pertanian Bogor. Tujuan penelitian adalah untuk mencari dosis zeolit yang optimal sehingga memberikan produktivitas yang optimal dan mengetahui pengaruh zeolit terhadap produksi tomat pada tanah Ultisol dari Jasinga dan Entisol dari Sindangbarang.
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Judul: Studi Pemamfaatan Mineral Zeolit sebagai Pengikat Amonium dan Pengaruhnya Terhadap Beberapa Sifat Kimia Media, Kadar Hara dan BObot Kering Tanaman Tomat (Lycopersicon esculentum Mill) Varietas Ratna
Abstrak: Penelitian ini bertujuan untuk mempelajari dan menge- tahui pengaruh pemberian zeolit, nitrogen, pupuk kandang dan kapur pada media terhadap pengikatan amonium, sifat kimia media, kadar hara dan bobot kering tanaman tomat. Penelitian menggunakan media campuran tanah Latosol Kotabumi (Oxic Dystropept), pupuk kandang dan kapur. Media A adalah tanah Latosol, media B tanah ditambah pupuk kandang dengan takaran 20 ton/ha, dan media C adalah tanah ditambah pupuk kandang dan kapur berdasar metode pH SMP. Setiap media kemudian ditambahkan mineral zeolit dan urea sebagai perlakuan. Zeolit yang digunakan adalah zeolit. Cikalong, didominasi klinoptilolit dan mordenit, berukuran 60 mesh yang telah diaktifkan melalui pemanasan pada suhu 250 °C selama dua jam. Takaran zeolit yang digunakan adalah 0, 10, 20, 40, dan 80 ton/ha. Takaran nitrogen yang digunakan adalah 100 dan 200 kg/ha dari sumber urea. Pupuk dasar yang diberikan adalah 200 ppm TSP, 200 ppm KC1, 5 ppm CuSO4, 5 ppm ZnSO4, 100 ppm MgS04, 1 ppm NH4M003, dan 2 ppm H3B03. Sebagai pembanding dicobakan pula media yang mengandung 10% tanah, 20 % pupuk kandang, dan 70 % zeolit (50% ukuran 25 mesh dan 50% ukuran 60 mesh) yang diperlakukan dengan nitrogen kg/ha dan 200 kg/ha. 100
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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