Judul: Konsentrasi logam berat  Fe, Cu, Mn, dan Ni terlalu dan pola sebarannya pada kondisi surut di perairan Muara Cimandiri, Pelabuhan Ratu
Abstrak: Karakteristik Sungai Cimandiri, Pelabuhan Ratu, dipengaruhi oleh salinitas, arus dan pasang surut, hal ini diprakirakan berpengaruh terhadap perubahan konsentrasi logam berat yang ada.
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Judul: KANDUNGAN LOGAM BERAT Cd, Cu, Ni, Pb DAN Zn TERLARUT DALAM BADAN AIR DAN SEDIMEN PADA PERAIRAN SEKITAR PELABUHAN PERIKANAN PELABUHAN RATU, SUKABUMI
Abstrak: Logam berat Cd, Cu, Ni, Pb dan Zn banyak ditemukan dalam cat, pigmen pada cat, pengawet kayu, minyak pelumas, komponen mesin, BBM dan karet kompond yang banyak digunakan sebagai penunjang aktivitas pelayaran di Pelabuhan Perikanan Pelabuhan Ratu. Hal ini memberikan potensi terjadinya pencemaran logam berat di perairan sekitarnya. Tujuan penelitian ini adalah untuk mengetahui konsentrasi logam berat Cd, Cu, Ni, Pb dan Zn pada daerah sekitar perairan Pelabuhan Perikanan Pelabuhan Ratu, Sukabumi. Pada penelitian ini dilakukan pengambilan contoh air dan sedimen sebanyak satu kali pada 8 titik stasiun di sekitar kolam Pelabuhan Perikanan Pelabuhan Ratu. Disamping itu juga diamati parameter fisika dan kimia perairan. Parameter fisika dan kimia air antara lain pH berkisar antara 7.97 hingga 8.01, salinitas antara 30 hingga 31 %o, suhu antara 29.5 hingga 30 °C, arus sebesar 2.034 hingga 2.138 cm/dt dan TSS antara 5.2 hingga 15.9 mg/l. Logam berat Cd dalam badan air sebesar <0.001 ppm, Cu antara <0.001 hingga 0.002 ppm, Ni antara <0.001 hingga 0.001 ppm, Pb dan Zn antra <0.001 hingga 0.003 ppm. Terlihat bahwa konsentrasi logam berat pada penelitian ini lebih rendah dari penelitian sebelumnya pada tahun 1993 kecuali untuk logam Ni yang relatif sama. Logam Cd dalam sedimen berkisar antara 0.037 hingga 0.342 ppm, Cu sebesar 12.865 hingga 47.418 ppm, Ni sekitar 4.562 hingga 9.188 ppm, Pb antara 7.822 hingga 28.899 ppm dan Zn sekitar 72.053 hingga 145.353 ppm. Dibandingkan dengan penelitian pada tahun 1992 konsentrasi logam berat Cu, Ni dan Pb pada penelitian ini menunjukkan nilai yang lebih rendah dan logam Cd dan Zn menunjukkan nilai yang lebih tinggi. Berdasarkan analisis pola sebaran terdapat indikasi bahwa kegiatan di dalam kolam pelabuhan perikanan menjadi potensi sumber dari logam berat namun masih belum menunjukkan pengaruh yang besar terhadap perairan disekitarnya. Hal ini dapat dilihat dari konsentrasinya yang relatif masih rendah dari rata-rata konsentrasi alami untuk perairan laut.
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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