Judul: Status Stok Ikan Tongkol (Euthynnus affinis Cantor, 1849) di Perairan Selat Sunda
Abstrak: Ikan tongkol (Euthynnus affinis) merupakan salah satu komoditas perikanan laut Indonesia yang utama dari kelas Scombridae. Meningkatnya permintaan pasar mendorong penangkapan ikan tongkol semakin tinggi sehingga dikhawatirkan dapat menurunkan stok ikan tongkol. Penelitian ini bertujuan untuk menganalisis status stok ikan tongkol di perairan Selat Sunda yang didaratkan di PPP Labuan, Pandeglang, Banten agar dapat menentukan rencana pengelolaan sumberdaya perikanan serta pemanfaatan ikan tongkol secara optimal dan berkelanjutan. Penelitian ini dilakukan di PPP Labuan, Pandeglang, Banten pada bulan Mei- September 2016. Teknik yang digunakan adalah pengambilan contoh acak berlapis. Hasil penelitian menunjukkan ikan tongkol di perairan Selat Sunda tidak dalam kondisi 1:1. Ikan tersebut memiliki pola pertumbuhan allometrik negatif. Ikan tongkol betina memiliki nilai koefisien pertumbuhan lebih besar dibandingkan ikan tongkol jantan. Ikan tongkol mengalami eksploitasi tinggi dengan mortalitas penangkapan lebih besar dari mortalitas alami. Melalui model Fox diperoleh upaya tangkapan aktual lebih besar dibandingkan upaya optimum sehingga terjadi overfishing.
Keyword: allometrik, eksploitasi, mortalitas, scomboidae

Judul: Status Stok Ikan Tongkol (Euthynnus affinis Cantor,1849) di Perairan Selat Sunda.
Abstrak: Ikan tongkol (Euthynnus affinis) merupakan ikan pelagis kecil yang memiliki nilai ekonomis penting di Perairan Selat Sunda. Tingginya permintaan masyarakat terhadap ikan tongkol mengakibatkan peningkatan penangkapan. Tujuan penelitian ini adalah untuk menentukan status stok ikan tongkol di perairan Selat Sunda. Penelitian ini dilakukan pada bulan April-Agustus 2015. Aspek yang dikaji meliputi rasio kelamin, hubungan panjang bobot, sebaran frekuensi panjang, identifikasi kelompok umur, ukuran pertama kali matang gonad, ukuran pertama kali tertangkap, parameter pertumbuhan, laju eksploitasi, dan model produksi surplus. Hasil penelitian menunjukkan bahwa ikan tongkol memiliki pola pertumbuhan alometrik positif. Upaya aktual tahun 2014 melebihi upaya optimumnya (fMSY). Laju eksploitasi ikan tongkol melebihi laju eksploitasi optimum (0,5), sehingga ikan tongkol diduga telah mengalami Overfishing dan overexploitation. Pengelolaan yang dapat dilakukan, yaitu pengaturan hasil tangkapan sesuai dengan jumlah tangkapan yang diperbolehkan dan pengaturan mesh size yang mengacu pada ukuran pertama kali matang gonad.
Keyword: ikan tongkol, laju eksploitasi, Overfishing, perairan Selat Sunda, pertumbuhan, upaya optimum

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