Judul: Aplikasi agar-agar rumput laut Gelidium sp. sebagai pengental pada formulasi sabun mandi cair
Abstrak: Permintaan konsumen terhadap sabun mandi cair cenderung meningkat setiap tahun jika dibandingkan sabun mandi padat. Kekentalan produk sabun mandi cair dapat diperoleh dengan menambahkan bahan pengental ke dalam pembuatannya. Penelitian ini bertujuan untuk mempelajari pengaruh penambahan agar-agar rumput laut Gelidium sp sebagai pengental sabun mandi cair pada berbagai konsentrasi. Tahapan pembuatan agar-agar rumput laut yaitu pembersihan dan pencucian, perendaman dan pemucatan, praperlakuan asam, ekstraksi I dan II, penyaringan (filtrasi), penjendalan, dan pengeringan. Pembuatan sabun mandi cair, dilakukan mencampurkan bahan-bahan penyusun, yaitu: sodium lauril sulfat, agar-agar, gliserin, sodium klorida, tetrasodium EDTA, asam sitrat, dan parfum secara bertahap dan homogen.
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Judul: Karakteristik Kombinasi Karaginan dan Tepung Rumput Laut Sargassum sp. sebagai Jelly Soap Non Gelatin
Abstrak: Rumput laut mengandung senyawa antioksidan yang potensial dan penggunaannya sudah meluas pada berbagai bidang industri, terutama kesehatan dan kosmetik. Hal ini yang mendorong untuk diciptakannya inovasi produk sabun jelly dengan menggunakan karaginan sebagai gelling agent dan tepung rumput laut coklat jenis Sargassum sp. sebagai sumber antioksidannya. Tujuan penelitian ini yaitu menentukan formulasi sabun jelly antioksidan terbaik dengan variasi penambahan karaginan dan tepung rumput laut. Penelitian ini terdiri dari beberapa tahapan. Tahapan pertama adalah pembuatan tepung rumput laut Sargassum sp. dan tahapan kedua yaitu pembuatan sabun jelly. Analisis yang dilakukan pada formulasi sabun jelly meliputi pengujian kadar air, nilai pH, pengujian aktivitas antioksidan, pengujian nilai asam lemak atau alkali bebas, dan juga penilaian sensori. Hasil yang diperoleh menunjukkan bahwa penambahan karaginan 7% membuat sabun memiliki tekstur yang lebih lunak namun tidak kenyal seperti jelly; pH 9.33; kadar alkali bebas 0%; kadar air 19.05% Penambahan 2% tepung Sargassum sp. memberikan nilai IC50 yang sangat lemah pada sabun, yaitu berkisar antara 5683.58 ppm hingga 5402.07 ppm.
Keyword: Sabun jelly, antioksidan, karaginan, Sargassum sp.

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