Judul: Kondisi fisika kimia sedimen dan struktur komunitas makrozoobenthos di Perairan Teluk sekitar zona industri Krakatau Steel, Cilegon
Abstrak: Penelitian dilakukan di perairan teluk sekitar Zona Industri Krakatau Steel, Cilegon, Propinsi Jawa Barat, bertujuan untuk mempelajari kondisi fisika kimia sedimen dan hubungannya dengan struktur komunitas makrozoobentos. Keadaan perairan teluk umumnya dangkal (kedalaman 0,5 sampai 6,5 m) dengan kecerahan yang beragam (0,5 sampai 2,0 m), sedangkan kecerahan di luar teluk mencapai 8,0 m. Suhu air tergolong normal, yaitu berkisar antara 31,0 sampai 32,0 °C. Namun suhu air di Teluk Gelebeg, terutama di sekitar tempat buangan limbah air panas (stasiun 2, 3, 4, 6, 7, 8, 9 dan 10) berkisar antara 34,0 sampai 35,0 °С. Salinitas berkisar antara 30,0 sampai 33,0%, yang menan- dakan massa airnya merupakan massa air laut lepas. Tekstur sedimen pada umumnya adalah liat (mengandung fraksi liat 30,21 sampai 77,14%, fraksi pasir 3,21 sampai 61,93% dan fraksi lumpur 5,69 sampai 35,32%). Nilai Eh sedimen pada lapisan permukaan adalah sebesar 190 mV sampai 155 mV dan pada lapisan bawah sebesar 210 mV sampai 100 mV. Nilai pH cenderung asam, pada lapisan permukaan pH berkisar antara 5,81 sampai 6,50 dan pada lapisan bawah antara 5,54 sampai 6,46. ...
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Judul: Distribusi horizontal beberapa parameter fisika kimia oseanografi selat Sunda di sepanjang pantai zona industri Krakatau Steel
Abstrak: Penelitian ini dilaksanakan bulan Mei sampai Juni 1992 di Zona Industri Krakatau Steel, Selat Sunda. Zona Industri Krakatau Steel terletak di Pantai Merak bagian Selatan dengan posisi 5°53'06'' sampai 6°02'08'' LS dan 105°59'08'' BT. Merupakan zona industri berat dan ringan yang menghasilkan berbagai limbah industri baik padatan, gas ataupun cairan. Parameter yang diukur dalam penelitian ini adalah suhu air, salinitas, kecepatan arus, kecerahan, kedalaman, PH, oksigen terlarut, kebutuhan oksigen biokimia, Fe, Mn, Zn, Cu, Cr, Pb dan Ni. Sedangkan pasang surut merupakan data sekunder. Pengolahan data dilakukan secara deskrip- tif dan indeks kesamaan "Canberra Metric". Suhu air permukaan di teluk berkisar antara 31.25- 32.55 °C. Sedangkan di luar teluk berkisar antara 30.25 30.77 °C. Salinitas di teluk berkisar antara 29.5 /oo. Sedangkan di luar teluk berkisar antara 31 - 31 32.25 °/00. Kecepatan arus di teluk berkisar antara 0.062 0.164 m/det dengan arah senatiasa keluar masuk teluk. Sedangkan di luar teluk berkisar antara 0.099 0.162 m/det dengan arah senantiasa menuju Barat Daya. perairan di teluk berkisar antara 0.73 Sedangkan di luar teluk berkisar antara 3 - Kecerahan 1.4 meter. 4.88 meter. Kandungan oksigen terlarut di teluk berkisar antara 4.38 5.17 ppm. Sedangkan di luar teluk berkisar antara 5 5.87 ppm. Kandungan BOD5 di teluk berkisar antara 4.37 4.74 ppm. 3.79 ppm. Sedangkan di luar teluk berkisar antara 3.12 Nilai pH di teluk berkisar antara 6.8 Sedangkan di luar teluk relatif sama yaitu 7.1. 7. Kadar Fe..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