Judul: Dampak Pencemaran Pertambangan Emas Tanpa Ijin terhadap Ikan Baung (Mystus nemurus) di Sungai Cikaniki, Kawasan Pongkor, Bogor.
Abstrak: Penelitian ini bertujuan umtuk mengetahui kadar merkuri dalam air sungai, sedimen, daging ikan dan organ tubuh ikan (insang, hati, ginjal) secara hispotalogis.
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Judul: Pencemaran Merkuri di Air, Sedimen dan Katak Tegalan (Fejervarya limnocharis) di Areal Sekitar Penambangan Emas Bogor, Jawa Barat
Abstrak: Area sekitar Pongkor Kabupaten Bogor, Jawa Barat merupakan daerah penambangan emas legal maupun illegal. Penambangan legal oleh PT Aneka Tambang (ANTAM) menarik perhatian masyarakat yang tinggal di sekitar lokasi untuk melakukan kegiatan pertambangan emas tanpa izin (PETI). Proses pencucian emas illegal ini biasanya menggunakan merkuri dan membuang sisa air cucian ke Sungai Cikaniki yang digunakan kembali untuk pengairan sawah. Walaupun telah dilakukan penelitian pencemaran merkuri di lokasi ini, namun kondisi terkini tidak diketahui. Oleh karena itu dilakukan analisis akumulasi pencemaran merkuri di air sungai, sedimen, dan katak tegalan (Fejervarya limnocharis) di Sungai Cikaniki. Pengambilan sampel air, sedimen sungai dan sawah serta katak tegalan dilakukan di tiga stasiun yang terletak di stasiun 1 (hulu sungai sebelum lokasi PETI), stasiun 2 (tengah yang berdekatan dengan dengan PETI ) dan stasiun 3 (hilir, setelah lokasi PETI). Kandungan merkuri ditemukan pada sampel air dan sedimen di Sungai Cikaniki dan area persawahan stasiun 2 dan stasiun 3. Hasil yang ditemukan menunjukkan bahwa kandungan merkuri pada air dan sedimen masih di bawah ambang batas minimum 0,002 ppm dan 0,17 ppm. Namun demikian, sampel hati katak pada stasiun 2 dan stasiun 3 telah terkontaminasi oleh merkuri dengan nilai di atas ambang batas, sementara hanya sebagian katak di stasiun 1 yang terkontaminasi. Hal ini menunjukkan adanya bioakumulasi merkuri pada tubuh katak walaupun faktor bioakumulasi dari air ke sedimen persawahan maupun dari sedimen persawahan ke katak tegalan tergolong rendah. Merkuri yang terbawa oleh air sungai sebagian teradsorbsi di sedimen sungai, sebagian terbawa melalui pipa-pipa dan masuk ke area persawahan sehingga teradsorbsi di sedimen sawah. Katak tegalan dapat terpapar melalui pernafasan, kulit dan pakan yang terkontaminasi.
Keyword: katak tegalan, merkuri, penambangan emas, Sungai Cikaniki

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