Judul: Emisi metana beberapa jenis rayap yang mengkonsumsi beberapa jenis kayu
Abstrak: Metana (CH4) merupakan salah satu gas rumah kaca yang panting karena mempunyai kontribusi yang cukup besar (15 %} terhadap pemanasan global, dan pertambahan konsentrasinya di atmosfir sangat pesat yakni meningkat sebesar 1 % per tahun. Gas tersebut juga sangat efektif menyerap sinar infra merah. Salah satu sumber gas CH4 di permukaan bumi adalah rayap (Insecta : Isoptera). Walaupun penelitian tentang biologi rayap sebagai hama telah banyak dilakukan di Indonesia akan tetapi belum ada data tentang emisi CH4 dari serangga tersebut. Padahal Indonesia merupakan negara dengan kelimpahan jenis rayap yang tinggi serta mempun yai ragam dan volume biomass (sebagai mak ana·n rayap) yang tinggi. suatu penelitian telah dilakukan untuk mengetahui : (1) fluks CH4 dari tiga jenis rayap (a) Macrotermes gilvus Hagen famili Termitidae, (b} Coptotermes curvignathus Holmg reen famili Rhinotermitidae, (c} Cryptotermes cyn6cephalus Light famili Kalotermitidae; (2) jumlah koloni bakteri anaerob yang terdapat pada suspensi hind gut (proctodeal, usus belakang) dari tiga jenis rayap yang diteliti; (3) kemampuan koloni bakteri anaerob menghasilkan gas pada hind gut dari tiga jenis rayap yang diteliti; (4) flu ks CH 4 dari Cryptotermes cynocephalus yang diberi kayu (a) Hevea brasiliensis Muell. Arg. (karet), (b) Elaies guineensis Jacq.(kelapa sawit), dan (c) Pinus merkusii Jungh et de Vriese (pinus); (5) hubungan antara fluks cH4 dari cryptotermes cynocephalus dengan laju konsumsi pada : (a) kayu ka ret, (b) kayu kelapa sawit dan (c) kayu pinus, oleh rayap; (6) fluks CH 4 dari koloni M. gilvus; dan (7) pengaruh ukuran koloni M. gilvus terhadap fluks CH4. ...
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Judul: Fluks metana (CH4) oleh tanah hutan di bawah tegakan rasamala (Altingia excelsa) di gunung Bunder BKPH Leuwiliang KPH Bogor Perum Perhutani Unit III Jawa Barat
Abstrak: Salah satu isu lingkungan yang dewasa ini dianggap penting oleh manusia adalah terjadinya pemanasan global global warming). Hal ini timbul karena terjadinya peningkatan gas-gas rumah kaca (GRK) di atmosfer. Gas- gas rumah kaca ini yang mengakibatkan efek rumah kaca (ERK). Gas metana merupakan gas rumah kaca kedua terpenting setelah CO2 (Salvato, 1992). Tanah di hutan tropis diketahui sebagai faktor yang penting dalam menentukan kadar metana di atmosfer. Hutan tropis yang mempunyai tanah kering berfungsi sebagai rosot metana karena memberikan kondisi yang baik bagi pertumbuhan dan perkembangan bakteri mechanotropic. Sebaliknya hutan yang tergenang merupakan sumber (sources) emisi metana cukup besar, karena kondisi ini cocok bagi bakteri methanogens yang menghasilkan metana. Penelitian ini bertujuan untuk mengetahui konsentrasi ambien metana, laju fluks metana, fluktuasi harian konsentrasi ambien metana dan faktor-faktor yang mempengaruhi laju fluks metana oleh tanah hutan di bawah tegakan Rasamala. ...
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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