Judul: Kontaminasi Logam Berat Pb dan Hg pada Ikan Nila (Oreochromis niloticus Linnaeus, 1758) Budidaya di Waduk Jatiluhur, Jawa Barat
Abstrak: Waduk Jatiluhur dimanfaatkan oleh masyarakat untuk budidaya ikan nila (Oreochromis niloticus). Meskipun begitu, waduk ini rentan terhadap limbah berbahaya dan beracun. Penelitian ini bertujuan menentukan kontaminasi kandungan logam berat timbal (Pb) dan merkuri (Hg) pada ikan nila yang dibudidayakan dan hubungannya dengan kualitas perairan di Waduk Jatiluhur. Analisis logam berat dilakukan dengan AAS. Kandungan logam berat tertinggi pada ikan nila adalah Hg pada ginjal yaitu 0,054 mg/kg. Kandungan logam berat tertinggi pada air yaitu Pb yaitu 0,263 mg/L. Nilai Bioconcentration Factor (BCF) untuk Pb dan Hg adalah 0,42 dan 211,11. Nilai MTI (Maximum Tolerable Intake) untuk orang dewasa (60 kg) untuk Pb dan Hg yaitu 16,67 kg/minggu dan 15,79 kg/minggu sedangkan pada anak-anak (15 kg) untuk Pb yaitu 4,17 kg/minggu dan 3,95 kg/minggu. Penelitian menunjukkan bahwa kadar DO dan timbal sudah tidak sesuai dengan baku mutu.
Keyword: air, ikan nila, logam berat, MTI, Waduk Jatiluhur

Judul: Analisis Kandungan Logam Berat Pb, Cu, dan Cd pada Ikan Nila (Oreochromis niloticus Linnaeus, 1758) Budidaya di Waduk Cirata, Jawa Barat
Abstrak: Waduk Cirata memiliki potensi yang besar baik dalam bidang ekonomi, maupun ekologi. Meskipun begitu, Waduk Cirata sangat rentan terhadap tekanan akibat aktivitas manusia, baik yang berasal dari dalam perairan waduk maupun dari Daerah Aliran Sungai (DAS) Citarum. Penelitian ini bertujuan menentukan kontaminasi kandungan logam berat timbal (Pb), tembaga (Cu) dan kadmium (Cd) pada ikan nila yang dibudidayakan dan hubungannya dengan kualitas perairan di Waduk Cirata. Konsentrasi logam berat dianalisis menggunakan AAS. Kandungan logam berat tertinggi pada ikan nila adalah Cu pada organ ginjal yakni 7,8 mg/kg. Kandungan logam berat tertinggi pada air yaitu Pb yakni 0,210 mg/L. Nilai Bioconcentration Factor (BCF) pada logam Pb sebesar 0,559, Cu 49,166 dan Cd 1,000. Nilai MTI (Maximum Tolerable Intake) untuk orang dewasa (60 kg) pada logam berat Pb sebesar 16,67 kg/minggu, Cd 0,13 kg/minggu dan Cu 11052,63 kg/minggu. Penelitian menunjukkan bahwa kadar Pb, Cu dan Cd tidak sesuai dengan baku mutu yang telah ditetapkan., Cirata Reservoir is great potential both in the economic and ecological fields. However, this Cirata Reservoir is very vulnerable to pressure due to human activities, both from within the reservoir waters and from the Citarum River Watershed (DAS). This study aimed to determine the contamination of the heavy metal content of lead (Pb), copper (Cu) and cadmium (Cd) in cultivated tilapia and its relationship with the quality of waters in the Cirata Reservoir. Heavy metal concentrations were analyzed using AAS. The highest heavy metal content in tilapia is Cu in the kidney, with concentration 7.8 mg/kg. The highest heavy metal content in water is Pb, with concentration 0.210 mg/L. Bioconcentration Factor (BCF) values of Pb 0.559, Cu 49.166 and Cd 1,000. The MTI (Maximum Tolerable Intake) value for adults (60 kg) for heavy metal Pb is 16.67 kg/week, Cd 0.13 kg/week and Cu 11052.63 kg/week. The research shows that the levels of Pb, Cu and Cd are not in accordance with the established quality standards.
Keyword: Cirata reservoir, heavy metals, nile, MTI, tilapia, water

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