Judul: Tingkat Kesuburan Perairan Situ Cigudeg Serta Hubungan Antara Produktivitas Primer dan Unsur Hara
Abstrak: Situ Cigudeg merupakan perairan terbuka sebagai penampungan air saluran pembuangan, yang terletak di Desa Cigudeg, Kabupaten Bogor. Tepatnya di pinggir jalan raya Bogor - Jasinga pada Km 36. Luas perairan Situ Cigudeg adalah 1,08 ha., dengan kedalaman maksimal3,7 m. Air yang masuk ke Situ Cigudeg mengangkut dan membawa berbagai beban masukan berupa bahan organik dan anorganik. Beban masukan tersebut merupakan sumber unsur hara serta sumber bahan partikel padatan. Penelitian ini betujuan untuk mengetahui tingkat kesuburan serta peranan unsur hara nitrogen dan fosfor perairan Situ Cigudeg. Hasil penelitian ini diharapkan dapat menjadi salah satu bahan masukan dalam upaya pemanfaatan sumberdaya perairan Situ Cigudeg.
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Judul: Ketersediaan unsur hara N dan P di Situ Cibuntu, Kecamatan Cibinong, Kabupaten Bogor, Jawa Barat
Abstrak: Perairan situ adalah salah satu tipe ekosistem perairan tawar tergenang dengan ukuran relatif kecil yang mempunyai banyak fungsi baik ditinjau dari segi ekologi maupun ekonomi. Berbagai jenis masukan baik dari luar maupun dalam situ dapat menyebabkan pengayaan nutrien yang berpengaruh terhadap kesuburan situ. Kualitas air sering dipakai sebagai pendekatan untuk mengetahui tingkat kesuburan perairan yang sangat ditentukan oleh ketersediaan unsur hara N dan P yang merupakan unsur esensial bagi pertumbuhan fitoplankton. Tujuan dari penelitian ini adalah mengetahui tingkat kesuburan perairan Situ Cibuntu berdasarkan ketersediaan unsur hara N dan P selama periode 1995 - 2003, mengetahui kondisi kualitas air Situ Cibuntu pada musim kemarau dan musim hujan dan mengetahui pengaruh rehabilitasi situ tahun 1998 dan pembuatan lahan basah tahun 2003 terhadap kondisi Situ Cibuntu saat ini. Berdasarkan kandungan unsur hara N dan P, Situ Cibuntu tergolong perairan oligotrofik-eutrofik. Secara umum kualitas air Situ Cibuntu masih layak untuk sumber air bagi kegiatan perikanan dan pertanian di sekitarnya. Kandungan unsur hara N dan P di Situ Cibuntu secara musiman berfluktuasi, kecuali konsentrasi total nitrogen yang cenderung turun pada musim kemarau dan cenderung naik pada musim hujan; nilai pH, oksigen terlarut dan suhu secara musiman relatif stabil; nilai bahan organik total cenderung berfluktuasi; nilai kecerahan pada musim kemarau cenderung naik, sedangkan pada musim hujan cenderung turun. Rehabilitasi situ tahun 1998 kurang berpengaruh terhadap kualitas air Situ Cibuntu; pembangunan lahan basah tahun 2003 dapat menurunkan konsentrasi total fosfor, klorofil-a dan bahan organik total, namun konsentrasi total nitrogen mengalami kenaikan.
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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