Judul: Peranan naungan azolla terhadap perubahan iklim mikro kolam serta dampaknya bagi pertumbuhan ikan nila (Tilapia nilotica)
Abstrak: Penelitian ini bertujuan untuk mengetahui peranan naungan azolla terhadap perubahan iklim mikro kolam serta dampaknya bagi pertumbuhan ikan nila (Tilapia nilotica). Dalam penelitian ini juga untuk melihat manfaat azolla sebagai makanan ikan nila dalam bentuk tumbuhan segar dan pelet yang terbuat dari azolla kering giling. Penelitian ini dilaksanakan di Areal Kolam Pemeli- haraan Ikan, Desa Sukamaju Baru, Kecamatan Cimanggis, Ka- bupaten Bogor. Lama penelitian adalah 3 bulan ditambah 1 bulan untuk persiapan perlakuan. Dimulai sejak awal bulan Oktober 1988 hingga akhir bulan Januari 1989. Dalam penelitian ini digunakan 2 faktor perlakuan, dengan 3 ulangan. Faktor pertama adalah perlakuan naungan azolla dan faktor kedua adalah perlakuan makanan tambahan. Rancangan yang digunakan adalah rancangan faktorial, de- ngan rancangan lapangnya yaitu rancangan acak kelompok. Peubah yang diamati meliputi suhu air kolam pada 3 level kedalaman dan intensitas cahaya surya yang diterus- kan masuk ke dalam kolam, yang diamati setiap hari pada Pagi, siang dan sore. Persen pengurangan azolla yang di- amati pada pagi hari, setiap 2 hari sekali. Derajad ...
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Judul: Evaluation of Azolla microphylla as Phytoremediator of Water Quality in Tilapia (Oreochromis niloticus) Reading Environment and its Growth Response
Abstrak: Pengurangan limbah nitrogen pada lingkungan budidaya dapat memanfaatkan teknologi fitoremediasi. Azolla microphylla dapat digunakan sebagai fitoremediator dalam lingkungan budidaya dengan mekanisme rhizodegradation. Penelitian ini bertujuan untuk mengevaluasi pemanfaatan A. microphylla terhadap parameter kualitas air dan respons pertumbuhan ikan nila. Rancangan percobaan yang digunakan adalah rancangan acak lengkap dengan tiga kali ulangan pada setiap perlakuan bobot azolla, yaitu 0, 15, 25, dan 35 g. Penelitian ini menggunakan benih ikan nila berukuran 6,86±0,98 g yang dipelihara dalam akuarium berukuran 100 x 40 x 30 cm bersama dengan tanaman azolla selama 30 hari dengan kepadatan 10 ekor tiap akuarium. Adapun volume air yang digunakan sebanyak 20 L. Hasil penelitian ini menunjukkan bahwa penggunaan bobot azolla sebesar 15 g dan 25 g memiliki konsentrasi amonia, nitrit, nitrat, dan total organic matter (TOM) yang lebih rendah dibandingkan kontrol (P>0,05) pada akhir penelitian. Tingkat kelangsungan hidup (TKH) dan rasio konversi pakan (RKP) tidak menunjukkan perbedaan nyata antar perlakuan (P>0,05), namun laju pertumbuhan spesifik (LPS) menunjukkan kontrol memiliki nilai terendah dibandingkan perlakuan lainnya (P<0,05). Perlakuan 15 g menunjukkan hasil terbaik pada parameter TKH dan RKP, sedangkan nilai LPS terbaik terdapat pada perlakuan 25 g diikuti perlakuan 15 g. Tingkat konsumsi oksigen dan kadar kolesterol cenderung meningkat pada akhir penelitian tetapi masih dalam batas normal. Penggunaan bobot azolla 15 g menunjukkan kinerja terbaik sebagai fitoremediator selama penelitian., Reduction of nitrogen waste in the cultivation environment can utilize phytoremediation technology. Azolla microphylla can be used as a phytoremediator in a aquaculture environment with a rhizodegradation mechanism. This study aimed to evaluate the use of A. microphylla on water quality parameters and growth response of tilapia. The experimental design used was a completely randomized design with three replications for each azolla weight treatment, namely 0, 15, 25, and 35 g. This study used tilapia seeds measuring 6.86 ± 0.98 g which were kept in an aquarium measuring 100 x 40 x 30 cm together with azolla plants for 30 days with a density of 10 fish per aquarium. The volume of water used was 20 L. The results of this study showed that the use of azolla weights of 15 g and 25 g had lower concentrations of ammonia, nitrite, nitrate, and total organic matter (TOM) than control (P> 0.05). at the end of the study. The survival rate (SR) and feed conversion ratio (FCR) did not show significant differences between treatments (P>0.05), but the specific growth rate (SGR) showed that control had the lowest value compared to other treatments (P<0.05). The 15 g treatment showed the best results on the SR and FCR parameters, while the best SGR value was found in the 25 g treatment followed by 15 g treatment. The level of oxygen consumption and cholesterol levels tended to increase at the end of the study but were still within normal limits. The use of azolla weight 15 g showed the best performance as a phytoremediator during the study
Keyword: Azolla microphylla, Fitoremediasi, Ikan nila, Kualitas air, Respons pertumbuhan

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