Judul: Keragaman Genus Fungi pada Tanah Hutan Mangrove Tercemar Logam Berat di Muara Angke, DKI Jakarta
Abstrak: Vegetasi mangrove merupakan vegetasi yang cenderung mengakumulasi logam-Iogam berat yang terdapat pada ekosistem yang bersangkutan. Hal ini tidak lepas dari peranan mikroba-mikroba tanah yang membantu tanaman untuk mengakumulasi logam-logam berat tersebut, baik mikroba yang mengkonsumsi logam itu sendiri ataupun mikroba yang bersatu dengan jenis tanaman tertentu untuk mengakumulasi logam berat tersebut. Semakin bertambahnya kegiatan industri telah menyebabkan semakin banyaknya jumlah limbah yang dihasilkan. Limbah industri ini menimbulkan masalah yang serius bagi ekosistem perairan karena menurunkan kualitas air yang akan berdampak pada ekosistem mangrove. Kegiatan-kegiatan tertentu telah menghasilkan limbah logam berat khususnya Hg, Pb dan Cu. Limbah logam berat yang tidak diolah masuk perairan laut dan mengalami pengendapan, pengenceran, dispersi dan metilasi oleh aktivitas mikroba dan selanjutnya akan diserap oleh organisme di perairan tersebut.
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Judul: Akumulasi logam berat (Mn, Zn, Cu.) pada jenis Rhizophora mucronata di hutan tanaman mangrove RKPH Cilacap BKPH Rawa Timur KPH Banyumas Barat, Perum Perhutani Unit I Jawa Tengah
Abstrak: Untuk menunjang pembangunan, banyak industri-industri dibangun tidak hanya di daerah industri tetapi juga di daerah pantai/teluk seperti halnya di Cilacap (pabrik se- men, pupuk, kilang minyak). Limbah yang dikeluarkan sudah tentu dapat berdampak pada lingkungan terutama jika menge- luarkan logam-logam berat. Akumulasi logam berat pada vegetasi mangrove dapat digunakan untuk mendeteksi akumulasi logam berat di eko- sistem tersebut. Penelitian ini dilakukan di Hutan Tanaman mangrove Cilacap dan analisis akumulasi logam berat dilakukan di Laboratorium Balai Penelitian dan Pengembangan Tanaman Pangan Bogor. Survey lapangan dilaksanakan pada bulan April 1994. Pengambilan contoh dengan cara komposit pada tiap-tiap lokasi pengamatan yang terdiri dari tiga kelom- pok (0-300 m, 600 900 m, 1200-1500 m menjauhi pabrik). Hasil penelitian menunjukkan bahwa akumulasi terba- nyak logam Mn di ketiga kelompok terjadi pada bagian daun (181.1 ppm, 187.4 ppm, 174.3 ppm). Akumulasi logam Zn terbanyak di kelompok I pada bagian akar (11.2 ppm), di kelompok II pada daun (7.6 ppm) dan di kelompok III pada bunga/buah (17 ppm). Sedangkan akumulasi logam Cu terba- nyak di ketiga kelompok terjadi pada akar (24.2 ppm, 5.5 ppm, 18.4 ppm). Dari ketiga kelompok pengamatan, akumulasi terbanyak tiap logam berat bervariasi. Logam Mn lebih banyak tera- kumulasi di kelompok II (357.4 ppm), logam Zn di kelompok III (36.6 ppm) dan logam Cu di kelompok I (349.7 ppm)....
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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