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: Sebaran spasial logam berat Pb, Cd, Cu, dan Zn dalam air dan sedimen di perairan pesisir dan beberapa Muara Sungai Kota Cirebon
Abstrak: Kegiatan pembangunan selama ini menimbulkan tekanan berat bagi lingkungan. Bentuk tekanan tersebut antara lain pencemaran lingkungan perairan oleh logam berat. Akumulasi logam berat yang berlebihan dalam air menyebabkan gangguan nyata maupun tersembunyi terhadap struktur dan fungsi ekosistem biota laut, anomali pada habitatnya; dan penurunan nilai perikanan dan pariwisata. Kondisi perairan pesisir Kota Cirebon yang padat penduduk, budidaya perikanan intensif, aktifitas pelabuhan berpotensi tercemar logam berat. Penelitian ini bertujuan mengkaji kandungan logam berat Pb, Cd, Cu, dan Zn di periaran pesisir dan beberapa muara sungai Kota Cirebon. Penelitian ini dilaksanakan di tiga muara Sungai yaitu Sungai Kalijaga, Kesunean, dan Sukalila pada 7 lokasi stasiun pengamatan. Pengambilan sampel dilakukan pada 2 November 2004 pukul 14.00-16.00 WIB. Analisis contoh dilakukan di Laboratorium Kelompok Penelitian Pencemaran P20 LIPI Jakarta pada 3-12 November 2004 menggunakan alat Atomic Absorption Spectrofotometer (AAS). Data yang diperoleh kemudian diolah dengan software ArcView 3.2 untuk dibuatkan sebaran spasialnya. Selain konsentrasi logam berat, pada stasiun pengambilan data yang sama juga diambil beberapa parameter fisika dan kimia laut yaitu suhu, salinitas dan pH. Nilai parameter suhu, salinitas, dan pH pada saat pengamatan nampak relatif seragam. Sebagai contoh nilai suhu berkisar antara 32-32,5 °C, salinitas berkisar antara 31,5-3300, dan pH berkisar antara 7,94-8,06. Berdasarkan parameter tersebut, kondisi perairan masih dapat dikatakan dalam kondisi normal. Hasil konsentrasi logam berat Pb, Cu, dan Zn di dalam air di muara Sungai Kalijaga, Kesunean, dan Sukalila pada saat pengukuran relatif kecil dengan kisaran sempit. Unsur Cd tidak terdeteksi dan batas deteksi alat untuk unsur Cd adalah adalah 0,001 mg/L. Konsentrasi Pb berkisar antara 0,003 0,004 mg/L, Cu antara 0,001-0,002 mg/L., dan Zn antara 0,006-0,016 mg/L. ...
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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