Judul: A-Optimal on Mixture Amount Design with modified of split plot design using point-exchange algorithm
Abstrak: Rancangan Jumlah Campuran (MAE) adalah yang terdiri dari komponen campuran dan jumlah total. Dalam prakteknya, komposisi campuran dijalankan pada setiap jumlah total campuran, akibatnya tidak dapat diacak sempurna, sehingga diperlukan pendekatan rancangan petak terbagi. Penelitian ini bertujuan untuk mengembangkan suatu algoritma untuk menemukan rancangan dengan kriteria A-Optimal untuk percobaan jumlah campuran dengan menggunakan modifikasi rancangan petak terbagi. Rancangan A-Optimal mencari rancangan yang meminimalkan kovarian parameter model. Studi kasus penelitian ini terdiri dari tiga bahan dan dua jumlah total campuran yang berbeda. Dalam penelitian ini, factor petak utama adalah jumlah total campuran, sedangkan faktor anak petak adalah komposisi campuran. rancangan A-Optimal dihasilkan berdasarkan model Second-Order Scheefe. Untuk Membangun rancangan A-optimal, menggunakan pendekatan algoritma point-exchange. Hasil dari algoritma ini menghasilkan komposisi optimum pada setiap jumlah total campuran.
Keyword: Point-exchange algorithm, A-optimality, mixture design, mixture amount experiment.

Judul: Rancangan D-Optimal pada Mixture-Process Variable dengan Pendekatan Petak Terbagi Menggunakan Algoritma Genetika
Abstrak: Experimental design is one of the fields of statistical science that is often used in the Industrial sector, especially in manufacturing product compositions. Experimental designs that are often used in the manufacture of product compositions are mixture experiments. A mixture experiment is an experiment that the factors are the components of a mixture. The response is assumed to depend only on the relative proportions of each component and not the total amount of the mixture (Cornell, 2002).. The unique feature in the mixture experiment is the mixture constraint. The proportion of each component must be between zero and one and sum to unity (Montgomery 2001).. In practice, Industrial experiments determining the proportion of product components are usually carried out by trial and error. However, trial & error is not efficient for experiments with many components. Therefore, designing an experiment can help researchers determine the right combination of product components and are more structured. In some cases, the response depends not only on the relative proportions of each component but also on process variables. These process variables are not part of the mixture, but if the conditions or levels are changed, these changes will affect the response of interest. This combination of mixture experiment and process variables is called Mixture Process Variable (MPV). In practice, the composition of the mixture experiment will run for each level of the process variables. The level of process variables may be hard to change from one level to another. Therefore, it causes a limitation in randomization. A split-plot approach can be an option to solve the problem, where the whole-plot is the process variables, and the sub-plot is the mixture component. The whole plot is a factor with hard to change, while the subplot is a factor with easy to change from run to run. In addition, the impact of MPV is a large number of experimental runs because the relative proportions of each component must be executed at each level of the process variable. Regularly the number of runs in an experiment must be limited due to cost, time, and resource constraints. However, choosing an optimal design with limited runs will ensure efficiency in such cases. The optimal design is part of the experimental design that estimates parameters without bias and minimum variance. Practically, the optimal design can reduce the cost and time in the experiment. The optimal design is searching for a good design based on a specific criterion. Finding an excellent optimal design concerning some optimality criteria can be difficult. In this study, a genetic algorithm (GA) was developed to find the optimal design. A genetic algorithm maintains a population of candidate solutions to a problem. Then choose the candidate who has the most suitable criteria to solve the problem. The selection criteria used is the D-optimality criterion based on parameter prediction. The D-optimal design is obtained by minimizing the variance value of the estimated model parameters. The case study was used to create the best recipe of two food recipes to accomplish this research with consisted of three mixture components and a process
Keyword: D-Optimality, Genetic Algorithm, Mixture Experiment, Process Variable, Split-plot

Judul: Posisi Filogenetik Kura-Kura Carettochelys Insculpta  (Reptillia: Testudinata)
Abstrak: Carettochelys insculpta is an endemic freshwater turtle of southern Papua and northern Australia. Phylogenetic position of this turtle is remain unresolved until now. Most of the research conducted show close relationships of C. insculpta and members of Trionychidae family. However, some of the research show distance between C. insculpta and members of Trionychidae family. This research is conducted to analyze phylogenetic relationship between C. insculpta and other members of Testudinata using 12S rRNA, 16S rRNA, and both gene as markers. DNA samples were obtained from three different organisms of C. insculpta. Two species used as outgroups were Alligator mississippiensis and Iguana iguana. Phylogenetic analysis was carried out using Neighbor-Joining method with Felsenstein 1981 (F81) modeltest. A polytomy tree of Testudinata was produced by using 12S rRNA gene. However, analysis using 16S rRNA and both genes as markers confirmed that C. insculpta was located between the Chelonidae and the Trionychidae family.
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