Judul: Uji Multilokasi Melalui Analisis AMMI Multirespon (Studi Kasus : Penelitian Galur Tanaman Tembakau Madura)
Abstrak: AMMI is used to analyze the data of multi location trials. The purpose of this analysis is to classify some stable or specific genotypes. The limitation of this analysis is only able to analyze for one response. Some methods of merged the responses can overcome this limitation. Several methods were considered good enough are range equalization, weighted based on principal component analysis, and division by mean. This study use the data of madura tobacco crop research in 2010-2012 were obtained from multi location trials involving 10 genotypes of tobacco plants and 9 environments. Measurement of responses includes the number of leaves, index of plant, chopped productivity, and quality index. The result of AMMI analysis for the origins responses shows that all genotypes are not stable at all origins responses. Only two genotypes are stable for two responses. The other genotypes grew most specifically expressed in each location. The Results of adaptability genotypes obtained by the combined response shows that just genotype C is stable in all combined responses and the other genotypes are grown in specific locations. Overall from the classification of genotypes, three combined responses are represented almost the entire of origins responses relatively. But from three methods were used, range equalization methods is the best choice for the data of this research.
Keyword: AMMI, combined response, Madura tobacco

Judul: Klasifikasi Genotipe Pada Data Tidak Lengkap  Dengan Pendekatan Model Ammi
Abstrak: Multilocations trials play an important role in plant breeding and agronomic research. Study concerning genotype-environment interaction needed in selection of genotype to be released. AMMI (Additive Main effect and Multiplicative Interaction) is one of statistical technique to analyze data from multilocations trials. The analysis of AMMI is a combining analysis between additive main effect and principal component analysis. The main restriction of using AMMI analysis is balance data. However a multilocations trials give an opportunity of the occurrence of unbalance data become very big. It is intended for every combination of genotype and location have the same number of replication. Therefore, we must estimate the data which do not complete. Incomplete data case, it is needed some data estimation method analysis, at this research employed connected data method and EM-AMMI algorithm to estimate incomplete data. Data which used in this research is obtained from Indon Spices Medicinal Crops Research Institute (ISMECRI) Bogor. It is a secondary data of ginger essential content resulted from multilocations trials, sixteen of genotype ginger which tested at five location in West Java. Simulation result shows that estimation using connected data will obtain smaller MAPE if its followed by two times EM-AMMI algorithm, for the percentage of data lose 10% to 20%. Production of ginger essential content estimated with AMMI2 model can explain 76.09% interaction structure among location and genotype. Seen from stability of genotype, there are 2 stable genotype, that is genotype 3 and 7. Genotype 8, 12, 15 specific at location B; genotype 9, 11, 14, 16 specific at location D and E; and genotype 1, 2, 4, 5, 6, 10 and 13 specific at location A and C.
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Judul: Hamiltonian formulation for linear waves motion in two layers fluid
Abstrak: The surface wave could be considered as a wave that separates two fluids, namely water and air. Based on this assumption, it is introduced the interfacial wave, a wave between two layers of fluid with different density. The formulation of interfacial waves motion begins with deriving the base equation of irrotational ideal fluid. Furthermore, according to irrotational fluid assumption, the base equation can be stated in velocity potential. In this derivation, the fluids domain is assumed to be restricted by rigid lid boundary conditions, both at the upper and lower limit. Therefore, the interfacial waves motion can be explained in a hamiltonian formulation. In the hamiltonian formulation, total energy is defined as the sum of kinetic and potential energy. The hamiltonian system is obtained from reduction of kinetic energy by using the Dirichlet Neumann Operator. The resulted kinetic energy equation is nonlinear. Therefore, this form is linearized by first part of the Taylor expansion. This linearization gives a dispersion relation of linear wave. Based on this dispersion relation, the phase speed of the linear wave depends on the density ratio of the two layers fluid.
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