Judul: Pengendalian kualitas ikan tuna untuk tujuan ekspor di Pelabuhan Samudera Nizam Zachman Jakarta
Abstrak: Ikan tuna merupakan salah satu komoditas eksporproduk perikanan Indonesia karena mempunyai nilai ekonomis penting, bernilai jual tinggi dan penghasilan pendapatan negara dari sektor perikanan. Namun saat ini, industri tuna masih memiliki permasalahan yang harus ditangani secara serius, diwarnai dengan persaingan yang semakin ketat dan berbagai isu global yang merebak seperti "food safety" dan isu lingkungan.
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Judul: Pengendalian Mutu Ikan Laut Segar Unggulan Utama yang Didaratkan di Pelabuhan Perikanan Samudera Nizam Zachman, Jakarta Utara
Abstrak: Pelabuhan Perikanan Samudera Nizam Zachman, Jakarta (PPSNZJ) merupakan salah satu PPS terbesar di Indonesia dengan produksi ikan yang cukup besar. Untuk mengetahui jenis komoditas ungulan utama di PPSNZJ, dilakukan analisis penetapan komoditas unggulan utama dengan menggunakan metode skoring. Data yang digunakan adalah data statistik 5 tahun terakhir PPSNZJ, yang meliputi data produksi, nilai produksi, harga komoditas, dan kontinuitas produksi. Berdasarkan hasil perhitungan, komoditas unggulan utama di PPSNZJ adalah tuna (Thunnus spp.) dan cakalang (Katsuwonus pelamis). Mutu tuna segar lebih baik dibandingkan dengan ikan cakalang segar. Untuk mengetahui nilai mutu dari jenis ikan unggulan di PPSNZJ, dapat dilakukan dengan menggunakan uji organoleptik, diagram pareto, diagram np dan p, serta diagram sebab akibat. Berdasarkan hasil penelitian, diketahui nilai rata-rata organoleptik tuna segar dan cakalang segar yang didaratkan di PPSNZJ masing-masing adalah 8 dan 4. Perbedaan nilai organoleptik tersebut disebabkan oleh penanganan kedua jenis ikan tersebut sangat berbeda. Sedangkan kerusakan atau cacat yang paling dominan adalah kerusakan daging dan beberapa organ tubuh lainnya, dengan nilai lebih dari 70%. Berdasarkan analisis diagram p, pengendalian tuna masih berada dalam batas pengendalian, sedangkan pengendalian cakalang segar sudah berada diluar batas pengendalian. Hal ini disebabkan oleh penanganan cakalang segar yang masih belum optimal, terutama dalam menjaga stabilitas suhu sewaktu ikan berada dalam palka. Selain itu, penyebab lain adalah penanganan pada saat di kapal yang mengakibatkan fisik cakalang saling bergesekan yang pada akhirnya mengalami kerusakan fisik cakalang.
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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