Judul: Kajian peranan ikan nilem (Osteochillus hasselti) dalam mengendalikan perifiton dan pengaruhnya terhadap kelangsungan hidup dan pertumbuhan ikan mas pada keramba jaring apung di waduk Cirata
Abstrak: This research has been carried out in Cirata man made lake, West Java intended to determine the appropriate density of silver minnow fish to control the periphyton population in the floating net cage (KJA) in order to maintain good water quality. It is expected that growth and survival rate of the common carp was normal. The research used Completely Randomized Design with the treatment were density of silver minnow fish (0, 100, 200, 300 and 400 fish/floating net cage). Silver minnow fishes + 5 g of weight were cultivated in the outer net with size 2 x 2 x 2 m, while inner net common carp fishes were cultivated with density of 200 fishes/cage and feed with artificial food of 3 times a day of 5% of body weight. This research is conducted during 3 month.result of the research showed that 100 fishes/cage density of silver minnow fish was able to maintain the periphyton population and resulting the best number of survival rate and growth rate common carp. .
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Judul: Pengaruh padat tebar terhadap pertumbuhan dan kelangsungan hidup ikan nilem (Osteochilus hassetlti C.V) yang dipelihara dalam keramba jaring apung di waduk Cirata dengan pakan perifiton
Abstrak: Saat ini waduk Cirata mengalami masalah serius seperti blooming algae, sedimentasi yang tinggi, biodiversitas ikan dan penurunan kualitas air. Penyebabnya antara lain dari limbah domestik atau rumah tangga, limbah pertanian dan limbah pabrik serta sisa pakan dan hasil metabolit ikan (CO2, NH3 dan H₂S) dari aktivitas KJA Algae (plankton) di waduk Cirata dapat dimanfaatkan dengan cara memelihara hewan-hewan pemakan plankton (plankton feeder) atau perifiton, salah satunya adalah ikan nilem Osteochilus hasselti C.V. Ikan ini tergolong herbivora yang aktif memakan plankton, perifiton dan berbagai jenis tumbuhan air. Beberapa petani ikan di Cirata telah menanam ikan nilem di keramba, namun hanya terbatas sebagai sambilan yang dipelihara pada jaring kolor bersama dengan ikan Nila. Saat ini percobaan ikan nilem yang meneliti pertumbuhan dan sintasan belum pernah dilakukan sebelumnya, oleh karena itu pada penelitian ini dilakukan percobaan terhadap ikan nilem dengan merujuk padat tebar ikan mas di KJA waduk Cirata. Penelitian ini bertujuan untuk mengetahui pengaruh padat tebar yang berbeda (35, 70 dan 105 ekor/m³) terhadap pertumbuhan, kelangsungan hidup dan FCR ikan nilem dalam KJA dengan pakan perifiton. Percobaan dilakukan pada bulan Desember 2004 hingga Februari 2005 di KJA Bonafeed, waduk Cirata, Cianjur. Benih ikan nilem (panjang 7,1 7,4 cm, bobot 4,6 5,3 g) dipelihara dalam 9 jaring berukuran 1x1x2 m. Selama pemeliharaan benih diberi perifiton yang terdapat pada jaring bekas berukuran 1x0,5 m. Perifiton tersebut didapatkan dengan cara merendam jaring bekas tersebut ke dalam air selama 7- 12 hari. Berat perifiton yang didapat setiap jaring bekas adalah 5 g (bobot kering). Pakan diberikan pada setiap kepadatan ikan masing-masing sebanyak 1,2 dan 3 lembar setiap pagi, siang dan sore. Peubah yang diamati adalah pertambahan bobot dan panjang, laju pertumbuhan harian, kelangsungan hidup, konversi pakan dan kualitas air. Penelitian ini menggunakan rancangan acak lengkap (RAL) dengan 3 perlakuan dan diulang sebanyak 3 kali, Uji Tuckey digunakan untuk menganalisis data peubah yang diamati. Benih ikan nilem mengalami pertambahan bobot dan panjang selama 48 hari pemeliharaan. Pada kepadatan 35, 70 dan 105 ekor/m³ masing-masing bobot rata-rata ikan bertambah dari 4,91 g menjadi 10,95 g; 4,79 g menjadi 9,05 g dan 4,83 g menjadi 7,35 g, pertambahan bobot tidak berbeda nyata (P>0,05). Sejalan dengan pertambahan bobot, rata-rata panjang ikan nilem selama pemeliharaan pada kepadatan 35, 70 dan 105 ekor/m³ masing-masing bertambah dari 7,32 cm menjadi 9,39 cm; 7,35 cm menjadi 8,45 cm dan 7,36 cm menjadi 8,00 cm juga tidak berbeda nyata antar perlakuan (P>0,05). Laju pertumbuhan harian ikan nilem pada kepadatan 35, 70 dan 105 ekor/m³ berturut- turut sebesar 1,66% 1,34% dan 0,88% (P>0,05). Sementara kelangsungan hidup ikan nilem pada kepadatan 35, 70 dan 105 ekor/m³ masing-masing adalah sebesar 99,52% 96,07% dan 95,56% (P>0,05). Pada kepadatan 35, 70 dan 105 ekor/m³, konversi pakan ikan nilem menunjukkan nilai berturut-turut sebesar 1,76; 2,35 dan 4,25 yang berbeda nyata antar perlakuan (P<0,05). IPB Univ Pada penelitian ini konversi pakan tertinggi dihasilkan dari kepadatan 35 ekor/m³ yaitu 1,76. Namun, mengacu pada pemanfaatan plankton dan perifiton....dst
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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