Judul: Model ammi terampat untuk data berdistribusi bukan normal
Abstrak: ALFIAN FUTUHUL HADI. Generalized AMMI for Non-Normal Data. Under supervision of AHMAD ANSORI MATTJIK and I MADE SUMERTAJAYA. AMMI (Additive Main Effect Multiplicative Interaction) model for interactions in two-way table provide the major mean for studying stability and adaptability through genotype × environment interaction (GEI), which modeled by full interaction model. Eligibility of AMMI models depends on that assumption of normally independent distributed error with a constant variance. In the study of genotypes’ resistance, disease and pest (insect) incidence on a plant for example, the appropriateness of AMMI model is being doubtful. Transform the observation by power family of Box-Cox transformation is an effort to handle the non-normality. AMMI model then can be applied to the transformed data appropriately following by the use of ordinary least square for estimating parameters. There is another way to handle this non-normality, i.e. by introducing multiplicative terms for interaction in wider class of modeling, Generalized Linear Models. An algorithm of iterative alternating generalized regression of row and column estimates its parameters. This model is known as GAMMI (Generalized Additive Main Effect Multiplicative Interaction) or GBMs (Generalized Bilinear Models). The multiplicative terms of GAMMI models can be visualized through Biplot, as in AMMI. A comparison of the two approaches above is investigated by applying them to a count data of pest population of Poisson distribution, which came from a study of leave pest in soybean genotype, and to study of rice genotype stability of filled grain per panicle (Binomial data). For data averages of filled grain per panicle, AMMI model for transformed data gives interaction matrix estimator slightly difference from of the one given by GAMMI logit-link model. R-square of Procrustes Rotation is less than 20 percent. On the other hand, for transformed data average of pest population, the two approaches do not give significant differences. Data exploration for the last case (the transformed data average of pest population) shows that its distribution character is very similar to Normal distribution. Therefore, it can be concluded that: (1) when the data distribution is close to Normal distribution, results of transformed AMMI and GAMMI are not significantly different; (2) when the data distribution is non Normal, results of the two approaches are quite different. Additional information on log-bilinear, which cannot be obtained from the transformed AMMI model, is odd ratio. This makes the GAMMI model superior compared to effort of transforming normality on Poisson distributed data.
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Judul: Pendugaan parameter model AMMI dengan komputasi menggunakan pendekatan bayes, Parameter estimation of ammi models with computation using  bayesian approach
Abstrak: Statistics on the application of plant breeding research has long used primarily in quantitative genetics. Modeling requirements for selection is needed to support efforts to obtain improved varieties. In modeling, there are two main paradigms used to estimate model parameters as the frequentist and Bayesian. Standard AMMI is a classical method has been used extensively for modeling and analysis genotype and environmental interactions. Homogeneity variance error is one of assumptions that must be satisfied in this method. Heterogeneity of variance error can lead to errors in conclusions regarding treatment effect. This study focuses attention on the computational efficiency of Bayesian in AMMI model parameters assumed in the data with heterogeneous variance error and evaluate the suitability of the configuration of genotype and environment interactions in Biplot AMMI. In the data with heterogeneous variance error, there are various differences between the treatment which is likely to cause a reduction in the efficiency of variance estimators in suspected treatment effect. Data transformation is usually used to overcome the problem of heterogeneity variance error. However, it is often quite difficult to obtain a suitable transformation and interpretations of treatment effect obtained from the transformation of data. Therefore we need another approach that can overcome the problem of heterogeneity variance error. The continued development of computerization, the Bayesian approach is a method that has been used to estimate parameters of linier-bilinier model. Bayesian approach is utilizing prior information about parameters to be expected and information from the sample that will be combined to get a posterior distribution. In this paper was evaluated the use of Bayesian approach to estimate model parameters and configuration AMMI biplot. There are two types of data used in this study, the simulated data and real data results of multilocation trials. Each type of data has homogeneous and heterogeneous variance. Prior distribution was a conjugate prior and values for posterior distribution were estimated by Gibbs sampling algorithm. The analysis showed that the Bayesian approach was quite efficient to estimate genotype and environment interaction effect. In fact, AMMI-BS using the BIC to determine the number of principal components of the interaction has a higher efficiency than AMMI-B. Bayesian approach to efficient enough in assuming an interaction effect can be seen from the variance that are smaller than standard AMMI. If the estimation of bilinier components of each method is used to construct the AMMI biplot to know the configuration of interaction structure, there are relatively similar in configuration among the three methods.
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Judul: Pembuatan dan Karakterisasi Prototipe Sel Surya Nanokristal n-TiO2/Dye/p-CuSCN
Abstrak: Dengan metode casting dan perlakuan annealing, dapat dibuat lapisan nanokristal semikonduktor TiO, pada substrat Transparant Conductive Oxide (TCO). Penambahan dye Methylviolet Thiocianate (MVSCN) dan lapisan CuSCN pada TiO, tersebut menghasilkan sel surya n-TiO,ldye/p-CuSCN yang dapat menyerap foton dan mengubahnya menjadi energi listrik.
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