Judul: Pengaruh dosis dan ukuran butir zeolit serta selang waktu penyiraman terhadap beberaa sifat fisik regosol dari sindangbarang
Abstrak: Percobaan ini dilakukan di rumah kaca dan laboratorium Fisika Tanah, Institut Pertanian Bogor dari bulan September 1987 sampai Maret 1988, dengan tujuan mengamati pengaruh pemberian zeolit dengan dosis dan ukuran butir yang berbeda yang dikombinasikan dengan beberapa selang waktu penyiraman terhadap indeks stabilitas agregat, bobot isi, distribusi ukuran pori, dan retensi air pada Regosol (Tropaquept Tipik) dari Sindangbarang. Rancangan yang dipakai dalam percobaan ini adalah. rancangan acak lengkap faktorial dengan 3 faktor, masing-masing diulang 3 kali. Faktor-faktor tersebut adalah Dosis zeolit (0, 15, dan 30 ton/ha), Ukuran butir zeolit (lebih kecil dari 0,5 mm, 0,5 - 2 mm, dan 2 5 mm), dan selang waktu penyiram- an (2, 3, dan 4 hari). Hasil percobaan menunjukkan bahwa perlakuan dosis zeolit meningkatkan indeks stabilitas agregat dan air tersedia. Di samping itu menurunkan rata-rata air lebih dan kadar air OF 4,2. Perlakuan ukuran zeolit meningkatkan rata-rata air lebih dan kadar air pF 4,2 serta menurunkan indeks stabilitas agregat. Pelakuan selang waktu penyiraman meningkatkan indeks stabilitas agregat dan pori drainase cepat. Di samping itu menurunkan rata-rata air lebih, bobot isi, pori drainase lambat, kadar air pF 2,54, dan air tersedia…
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Judul: Pengaruh ukuran butir dan tingkat pemanasan zeolit terhadap kemantapan agregat, kemampuan tanah menahan air dan bobot isi tanah pada tropudult dari Jasinga
Abstrak: Penelitian ini bertujuan untuk mempelajari ukuran pengaruh dan tingkat pemanasan zeolit terhadap kemantapan agregat, kemampuan tanah menahan air dan bobot isi tanah pada Tropudult dari Jasinga. Perlakuan yang ukuran < 100 μ. 100 diberikan yaitu zeolit dengan 500 μ, 500 1000 µ, dan 1000 - 2000 μ, yang masing-masingnya dipanaskan dengan suhu 150 °C, 250 °C, 350 °C, 450 °C. dan 550 °c, selama empat jam. Dosis yang digunakan ialah enam ton per Ha. Tanah yang telah diberikan perlakuan dinkubasi selama tiga bulan dan dilakukan penyiraman untuk menjaga kapasitas lapang. Hasil sidik ragam menunjukkan baik perlakuan besar ukuran butir maupun tingkat pemanasan zeolit tidak berpengaruh nyata terhadap Indeks Stabilitas Agregat, Bobot Isi tanah, Kadar Air pada pF 1.00, pF 2.00, pF 2.54, pF 4.20, dan Air Tersedia. Walaupun demikian ukuran yang semakin halus menunjukkan adanya penurunan nilai Bobot Isi tanah. Demikian pula pada perlakuan tingkat pemanasan zeolit suhu pemanasan yang semakin rendah menunjukkan adanya penurunan nilai Bobot Isi tanah, peningkatan Indeks Stabilitas Agregat, Kadar Air pada pF 1.00, pF 2.00, pF 2.54, pF 4.20, dan Air Tersedia.
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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