Judul: Kondisi kualitas perairan di Pelabuhan Sunda Kelapa, DKI Jakarta
Abstrak: Pelabuhan Sunda Kelapa merupakan salah satu bgian dari Teluk Jakarta, dimana daerah yang memiliki aktivitas yang cukup padat, pelabuhan perdagangan serta pelabuhan penyeberangan nasional.
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Judul: Analisis kebijakan pengelolaan lingkungan pelabuhan sunda kelapa DKI Jakarta
Abstrak: Sunda Kelapa is a port of Jakarta bay. It have a high contaminated, that give inaccurate policy of environmental management signal. The aim of research is to create a scenario of environmental management policy for Sunda Kelapa with ecology dimension, economy, cultural social and the institution approach. This research did by survey methode that consist of primary and secondary data. The analysis include; water quality analysis and multi criteria decision making (MCDM) analysis. MCDM analized by simple multi attribute rating technique (SMART) and visual interactive sensitivity analysis technique (VISA). The result of water quality analysis showed that, brightness, turbidity, H2S, N03, BOD, Pb and Cd are more than maximum level of the Regulation. And there are organic and inorganic garbage and also there are oil coat. Analysis of biological parameter include macrozoobenthos and phytoplankton. Mactra (macrozoobenthos) and Skeletonema (phytoplankton) were dominated the Sunda Kelapa Aquatic. This condition showed us that Sunda Kelapa Port had been contaminated/polluted. The result of assessment to level of importance obtained showed that economy criteria have more important criteria compare to ecology, cultural social and institution criteria. The result of multi criteria decision making (MCDM) showed that exploiting of Sunda Kelapa port as loading and unloading take especial priority as The policy scenario of Sunda Kelapa Port that will be developed.
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Judul: Pengembangan kekekaran Model Additive Main Effect – Multiplicative Interaction (AMMI)
Abstrak: AMMI model for interactions in two-way table provide the major mean for studying stability and adaptability through GEI, which modeled by full interaction model. Eligibility of AMMI model depends on the assumption of normally independent distributed error with a constant variance. Nowadays, AMMI models have been developed for any condition of MET data i.e the violence of normality and homegeneity assumptions. We can mention in this class of modelling as MAMMI for Mixed AMMI model and GAMMI for Generalized AMMI model. GAMMI model handles non-normality i.e categorical response variables using an algorithm of alternating regression. While handling the non-homogeneity in mixed-models sense, one may use a model called factor analytic multiplicative for a MAMMI models. Outlier might be found in the data coincides with non-homogeneity variance. A method of handling outlier in additive and multiplicative modeling by applying Robust Alternating Regression (RAR) in FANOVA model. RAR FANOVA model was downweighting outlying scores and loadings in the k-dimensional spaces of scores and loadings, and robust estimator will be minimized under the constraints that are consistent with robust approach of the median of parameters. Application of GAMMI was found in several distribution of exponential family. The most interesting here is Poisson distribution which it has a unique property of equal mean and variance. Many zero observations make some dificulties and fatal consequence in Poisson modeling. We consider to facilitates an analysis of two-way tables of count with many zero observations in AMMI model. We develop GAMMI model for Poisson with zeroes problem, by a statistical framework of RCAM. Some link function apply to the mean of a cell equalling a row effect plus a column effect plus interaction terms are modelled as a reduced-rank regression with rank of 2, then it will be visualized by Biplot through SVD reparameterization. The ZIP-GAMMI model handle the excess-zero and also the overdispersion at once. The interaction structures are extracted from the non-zero cell. ZIP model provide us the probability become zero and the fitted value of the Poisson. The modelling scheme involves two important things: (1) the distribution (and link-function) and (2) the rank of model. Both are confounded, especially if there is overdispersion or excess-zero. Best-model fit can be provided by proper link function with respect to data’s distribution, at the same time it’s also possible by the rank of the model in decomposing the interaction terms. In this case the likelihood ratio test provides us the hypothesis testing.
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