Judul: Kajian kualitas lingkungan perairan dan penggunaan komunitas makrozoobenthos dalam penentuan status pencemaran di Waduk Saguling
Abstrak: Tujuan penelitian untuk mengkaji kualitas lingkungan perairan Waduk Saguling pada bulan Juli, Agustus dan September 2004 dan mengetahui status pencemaran dengan menggunaka komunitas makrozoobenthos. Parameter fisika-kimia perairan yang dikaji meliputi suhu, kekeruhan, TSS, TDS, pH, DO, NO3-N dan PO4-P.
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Judul: Komposisi dan kelimpahan plankton berbagai kedalaman di Waduk Saguling pada musim kemarau tahun 2004
Abstrak: Waduk Saguling menerima masukan dari DAS Citarum dan anak sungainya dengan beban limbah yang tinggi. Selain itu tingginya aktivitas budidaya karamba dapat mempengaruhi kehidupan organisme yang terdapat didalamnya. Penelitian bertujuan untuk mengetahui peranan plankton sebagai indikator biologi dan suksesi ekologi di Waduk Saguling. Penelitian dilaksanakan pada bulan Juli, Agustus, September tahun 2004, terdiri dari 4 stasiun dan 4 kedalaman yaitu permukaan, 1 m, 2 m, dan 3 m. Hasil analisis selama penelitian ditemukan lima kelas fitoplankton yaitu Cyanophyceae (7jenis), Bacillariophyceae (12 jenis), Dinophyceae (2 jenis), Euglenophyceae (1 jenis), Chlorophyceae (16 jenis). Zooplankton terdiri dari empat kelas yaitu Monogononta (7 jenis), Copepoda (5 jenis), Cilliata (3 jenis), Branchiopoda (4 jenis). Kelimpahan plankton (fitoplankton dan zooplankton) rata-rata terbesar terdapat pada stasiun 1, 2, dan 3. Sedangkan stasiun 4 kelimpahannya rendah, hal itu diduga karena pengaruh kondisi perairan yang kurang mendukung. Indeks keanekaragaman (H') fitoplankton dan zooplankton berkisar antara 0,5373-2,2532 dan 0,1358-2,1656, nilai indeks keseragaman (E) fitoplankton dan zooplankton berkisar antara 0,2063-0,7823 dan 0,1959-1, nilai indeks dominansi (D) fitoplankton dan zooplankton berkisar antara 0,1432- 0,8027 dan 0,1318-0,9412. Berdasarkan parameter kualitas air yang diamati secara umum parameter TSS, kekeruhan, nitrat, ortofosfat rata-rata nilainya tinggi sedangkan DO dan kecerahan rendah. Hasil regresi linier berganda menunjukkan bahwa parameter fisika kimia berpengaruh terhadap kelimpahan fitoplankton sebesar 34%. sedangkan kelimpahan zooplankton dipengaruhi sebesar 37% oleh parameter fisika kimia yang diamati. Secara umum pola suksesi di Waduk Saguling setiap bulannya terjadi perubahan stadia, perubahan ini dipengaruhi oleh kondisi kualitas perairan.
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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