Judul: Kajian ketersediaan oksigen dan kaitannya dengan beban organik di perairan estuari sungai cisadane
Abstrak: Estuari Cisadane adalah muara dari S. Cisadane, salah satu sungai besar di Jawa, yang alirannya melintasi daerah pemukiman yang luas dengan beragam kegiatan, yaitu kota Bogor dan Tangerang. Limbah dari berbagai kegiatan perkotaan tersebut, diduga telah menyebabkan meningkatnya beban limbah organik mudah urai (biodegradable organic wastes) sehingga berakibat pada penurunan kualitas air, antara lain kondisi hypoxia (kadar oksigen terlarut ≤ 3 mg/L), di estuari. Kondisi hypoxia di estuari dapat terjadi sehubungan dengan penggunaan oksigen yang tinggi terutama untuk dekomposisi bahan organik. Tingginya beban bahan organik ini terutama akibat limbah berbagai kegiatan manusia (antropogenik) di sepanjang aliran sungai, yang terbawa aliran dan tertahan oleh arus pasang-surut laut, khususnya di musim kemarau saat debit aliran sungai rendah, sehingga dapat terakumulasi pada zona tertentu di estuari.
Keyword: Biodegradable organic wastes, Least square analysis

Judul: Peluruhan Bahan Organik saat Musim Kemarau pada Bagian Payau dan Laut di Muara Sungai Cisadane Tangerang, Banten
Abstrak: Aktivitas manusia di sepanjang DAS Cisadane memberikan kontribusi baik secara langsung maupun tidak langsung terhadap peningkatan bahan organik. Bahan organik ini akan terbawa aliran air hingga menuju muara sungai. Kandungan bahan organik yang tinggi di muara sungai dapat meningkatkan BOD sehingga dapat menurunkan kadar oksigen terlarut pada daerah tersebut. Apabila laju pemanfaatan oksigen untuk proses dekomposisi tidak menyebabkan kadar oksigen turun di bawah batas minimum yang aman, maka kondisi perairan masih belum terganggu, namun apabila laju pemanfaatan oksigen tersebut telah menyebabkan kadar oksigen turun dibawah batas minimum, maka kondisi perairan akan terganggu dan perlu dilakukan pengendalian terhadap pencemaran bahan organik. Penelitian ini dilakukan untuk mengkaji laju peluruhan bahan organik saat musim kemarau pada bagian payau dan laut di muara Sungai Cisadane. Penelitian dilakukan pada bulan Juni-Agustus 2008. Lokasi penelitian berada di Muara Sungai Cisadane, Desa Tanjung Burung, Kecamatan Teluk Naga, Kabupaten Tangerang, Propinsi Banten. Dalam penelitian ini terdapat dua titik pengambilan sampel (stasiun), stasiun satu (ST.1) adalah daerah percampuran antara air tawar dan air laut yang masih berada di dalam daerah aliran sungai, sedangkan stasiun dua (ST.2) adalah daerah yang relatif sudah tidak terpengaruh oleh pengenceran dari air tawar dan berada di lepas pantai.
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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