Judul: Kajian Sistem Pengendalian Bahan Baku Teh Botol Frestea Pada PT. Coca-Cola Bottling Indonesia Central Java Semarang
Abstrak: Dewasa ini industri minuman teh botol semakin diminati dan tumbuh seiring dengan meningkatnya minat masyarakat terhadap minuman siap saji. Dalam kurun waktu dua tahun terakhir, persaingan di industri minuman teh botol semakin ketat dengan masuknya frestea sebagai pemain baru. Meningkatnya jumlah perusahaan teh siap saji menyebabkan perusahaan harus bersaing untuk mendapatkan konsumen, dengan mutu produk yang baik dan harga terjangkau. Hal ini harus didukung oleh pengadaan bahan baku yang optimal dan berkesinambungan sehingga kontinuitas produksi dapat terjamin dengan demikian dapat menjamin pemenuhan kebutuhan masyarakat.
Keyword: 

Judul: Analisis preferensi konsumen terhadap frestea teh kita dan teh sosro kemasan botol di Kota Bogor
Abstrak: Teh merupakan minuman yang mudah dalam mendapatkan bahan bakunya serta minuman khas (tradisional) bagi penduduk Indonesia. Teh dijual dalam berbagai bentuk yaitu (1) dalam bentuk teh siap minum yang dikemas dalam kemasan karton dan kemasan botol, (2) dalam bentuk curah dimana konsumen harus menyeduhnya dan mengeluarkan ampasnya atau yang lebih dikenal dengan teh celup. Frestea merupakan salah satu produk teh kemasan botol yang diproduksi oleh PT. Coca-Cola Bottling Indonesia. Minuman ini termasuk dalam minuman teh siap saji beraroma melati yang dikemas dalam botol dan tetra-pack (kotak). Frestea adalah produk teh kemasan botol yang masih baru dikeluarkan perusahaan yaitu pada bulan Juni 2002, sebagai produk yang masih baru dipasaran perusahaan perlu mengetahui bagaimana perilaku konsumen terhadap Frestea kemasan botol untuk dapat menemukan strategi yang tepat untuk menghadapi persaingan minuman teh dalam kemasan.
Keyword: 

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.
Keyword: