Judul: Keragaman Genus Fungi pada Tanah Hutan Mangrove Tercemar Logam Berat di Muara Angke, DKI Jakarta
Abstrak: Vegetasi mangrove merupakan vegetasi yang cenderung mengakumulasi logam-Iogam berat yang terdapat pada ekosistem yang bersangkutan. Hal ini tidak lepas dari peranan mikroba-mikroba tanah yang membantu tanaman untuk mengakumulasi logam-logam berat tersebut, baik mikroba yang mengkonsumsi logam itu sendiri ataupun mikroba yang bersatu dengan jenis tanaman tertentu untuk mengakumulasi logam berat tersebut. Semakin bertambahnya kegiatan industri telah menyebabkan semakin banyaknya jumlah limbah yang dihasilkan. Limbah industri ini menimbulkan masalah yang serius bagi ekosistem perairan karena menurunkan kualitas air yang akan berdampak pada ekosistem mangrove. Kegiatan-kegiatan tertentu telah menghasilkan limbah logam berat khususnya Hg, Pb dan Cu. Limbah logam berat yang tidak diolah masuk perairan laut dan mengalami pengendapan, pengenceran, dispersi dan metilasi oleh aktivitas mikroba dan selanjutnya akan diserap oleh organisme di perairan tersebut.
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Judul: Kandungan logam berat (Pb dan Cd) pada tegakan pidada (Sonneratia alba) di hutan mangrove cagar alam Muara Angke DKI Jakarta
Abstrak: Berbagai jenis industri membuang limbahnya ke sungai yang mengalir ke pantai sehingga mengakibatkan beban pencemaraan di daerah pantai semakin tinggi dan akan menyebabkan penurunan kualitas lingkungan. Salah satu bahan pencemar yang berasal dari limbah industri adalah logam berat. Lingkungan perairan yang tercemar oleh logam berat akan menyebabkan terkontamiasinya berbagai biota di dalamnya, seperti tanaman mangrove yang berada di kawasan Cagar Alam Muara Angke. Penelitian ini dilaksanakan dengan tujuan untuk menge- tahui kemampuan menyerap dan akumulasi logam berat (Pb dan Cd) di bagian akar, batang dan daun pada tegakan pidada (Sonneratia alba). Metode pengambilan sampel pohon dengan membuat dua buah jalur yang memanjang. Pohon sampel yang diambil merupakan perwakilan dari kelas diameter < 10 cm, 10 20 cm, 21 - 30 cm, dan > 31 cm. Bagian pohon yang diambil adalah bagian akar, batang dan daun sebanyak 100 200 gram. Nilai kandungan logam berat (Pb dan Cd) hasil analisis laboratorium di uji dengan metode Kruskal Wallis untuk mengetahui beda nilai kandungan logam berat (Pb dan Cd) pada tiap-tiap bagian pohon, dan metode Wilcoxon Sum Rank Test untuk menguji beda kandungan logam Pb dan Cd pada tiap-tiap pohon pidada. ...
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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