Judul: Dynamics system modeling for rice stock
Abstrak: Produksi beras di Indonesia dicirikan oleh banyaknya pihak yang terlibat dan luasnya wilayah distribusi. Terdapat dua sumber utama pengadaan beras di Indonesia, yaitu produksi dalam negeri dan beras impor. Dua jenis beras tersebut didistribusikan ke seluruh negeri melalui berbagai pemasaran dan alat distribusi. Dinamika pasokan beras dan distribusinya terutama dipengaruhi oleh musim, luas panen, produktifitas lahan, harga di dalam negeri, harga beras di luar negeri, bea masuk, tindakan pemerintah melalui Perum BULOG, dan kualitas infrastruktur. Penelitian ini bertujuan untuk menghasilkan sebuah model sistem dinamis yang dapat digunakan untuk mempelajari perilaku sediaan beras di Indonesia sepanjang tahun. Metode yang digunakan adalah metoda simulasi dengan memanfaatkan perangkat lunak ithinkTM. Sistem distribusi beras diuraikan menjadi sepuluh subsistem yang meliputi sub sistem petani, pedagang pengumpul, penggilingan padi, Koperasi Unit Desa (KUD), grosir/swasta, importir, Bulog, stok nasional, pengecer dan subsistem konsumen atau pengguna akhir. Interaksi antar subsistem divisualkan dengan diagram kausal (causal diagram) yang disusun mengikuti pendekatan sistem. Sensitifitas model dapat diperiksa dengan menjalankan model menggunakan satu set nilai awal parameter dan peubah utama, serta memvariasikan nilai salah satu peubah.
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Judul: Dynamics System Modeling for Rice Stock
Abstrak: Rice production in Indonesia is characterized by large number of people and institutions involved in the business and wide spread of distribution area. There are two kinds of rice sources namely in country farmers production and the imported one. From these two sources rice is distributed throughout the country through many channels of marketing and distribution. The dynamics of rice supply and its distribution is mainly affected by factors of season, harvest field availability and its productivity, rate of harvesting failure, in country prices, import barrier, government involvement, and quality of infrastructure. A system dynamics model of rice stock was constructed using ithinkTM software. The model attempts to figure out the behaviour of rice stock system by simulation technique. The system was represented by ten subsystems which are interacting one to another. These ten subsystems were farmers, rice millings, KUD, collector traders, Bulog, grociers, importers, national stock. The interaction between each subsystem is depicted by a causal diagram, following the systems dynamics approach. The sensitivity of the model can be tested by running the model under certain set of values of key parameters and variables. The simulation resulted in two important findings. First, the model was able to picture the dynamics of rice distribution among ten subsystems assigned. Second, the value of stocks at each subsystem were considerably resembling the actual stocks at each subsystem. These results enabled the model to be used as a predicting tool or simulation tool for rice stock distribution.
Keyword: Rice stock, dynamic system

Judul: Synthesis of Apatite - Chitosan Composite Using Duck Eggshell and Chitosan with Temperature and pH Control
Abstrak: Composite of apatite-chitosan was prepared by using chitosan as polymer matrix. Calcium phosphate was prepared from CaO and (NH4)2HPO4, which the ratio Ca : P had to be 1.67. Chitosan is produced by deacetilase from biopolymer chitin. Duck eggshell (95% CaCO3) as starting material was converted to be calcium oxide (CaO) through calcinations with temperature 1000oC for 5 hours. In-situ and ex-situ methods were prepared from apatite and chitosan (dissolved with acetate acid 2%). Calcium phosphate prepared for bone implantation, but it is too fragile and brittle to be implant into the body. So addition of chitosan can decrease crystalinity and increase mechanic characteristic (tougher). Composite apatite-chitosan was resulted by fast precipitation. Fast precipitation was held in a breaker glass with nitrogen atmosphere, under controlled temperature at 37oC and pH 7, in order to increase amount of hydroxyapatite. Precipitation with CaO which was dropped by (NH4)2HPO4. Hydroxyapatite and chitosan were appearing at samples with in-situ and ex-situ methods which indicating by peak matching of XRD (closed lattice parameter, crystal size), overlapping at FTIR spectra and supporting by SEM picture (homogenous and agglomerated particle). However, it still needs further research to examine biocompatibility osteoconductive degree of sample by in-vivo and in-vitro test.
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