Judul: Sirup Fruktosa sebagai Hasil Samping Pembuatan Tepung Beras Berprotein Tinggi
Abstrak: Tepung beras berprotein t i n g g i (TBPT) adalah salah satu bahan pangan olahan yang dapat digunakan untuk pemenuhan kebutuhan protein. Proses pembuatan TBPT menghasilkan hasil samping berupa h i d r o l i s a t pati. Penelitian ini bertujuan untuk memanfaatkan hasil samping tersebut sebagai bahan baku pembuatan sirup fruktosa.
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Judul: Evaluasi nilai gizi protein tepung beras berprotein tinggi secara in vitro
Abstrak: Peningkatan kandungan protein beras dapat dilakukan dengan memanfaatkan teknologi enzim. Komponen pati diuraikan secara enzimatis dengan menggunakan enzim amilase, sehingga bagian protein beras akan terkonsentrasi bersama sisa pati. Tujuan penelitian ini adalah untuk melakukan evaluasi nilai gizi protein Tepung Beras Berprotein Tinggi secara in vivo. Enzim yang digunakan dalam pembuatan Tepung Beras Berprotein Tinggi ini diperoleh dari fermentasi menggunakan kapang Aspergillus oryzae. Fermentasi dilakukan selama tiga hari dengan menggunakan media fermentasi dedak. Spora kapang ditambahkan sebanyak 4 persen dengan jumlah total kapang 1.05-2.25 x 1012. Enzim yang dihasilkan dari fermentasi ini adalah -amilase, amiloglukosidase dan sedikit enzim protease. Enzim -amilase yang dihasilkan mempunyai aktivitas tertinggi 11.37 unit/ml dan aktivitas rata-rata 11.32 unit/ml. Aktivitas tertinggi untuk enzim amiloglukosidase adalah 1.35 unit/ml dengan rata-rata aktivitas sebesar 1.18 unit/ml. Aktivitas enzim protease yang dihasilkan adalah 0.085 unit/ml. Pembuatan Tepung Beras Berprotein Tinggi dilakukan dengan melakukan penambahan enzim sebanyak 1.5 ml enzim/ml suspensi tepung beras dengan waktu hidrolisis 80 menit pada suhu 60°C. Tepung Beras Berprotein Tinggi yang dihasilkan mempunyai kandungan protein rata-rata 20.58 persen, lemak 1.64 persen, abu 1.12 persen, serat kasar 2.07 persen, air 8.72 persen dan karbohidrat 65.87 persen. Kadar protein Tepung Beras Berprotein Tinggi meningkat hampir tiga kali lipat dibandingkan kadar protein bahan asal yaitu tepung beras (7.39 persen). Rendemen Tepung Beras Berprotein Tinggi yang dihasilkan sebesar 32.78 persen, sedangkan rendemen proteinnya sebesar 86.54 persen…dst
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Judul: Wetland Change Prediction Using Markov Cellular Automata Model In Lore Lindu National Park Central Sulawesi Province, Indonesia (Seminar)
Abstrak: Lore Lindu National Park (LLNP) is located between 119°90’ - 120°16’ East and 1°8’ - 1°3’ South and administratively, located in Poso District and Donggala District, in Central Sulawesi Province, Indonesia. Topographically, Lore Lindu National Park has two enclaves: Lindu Enclave (13.093 ha) and Besoa Enclave (5.085 ha). Lindu enclaves have high growing socio economic activities that can threaten Lore Lindu National Park as a tropical rainforest that requires the stability of rainforest margin area. If agricultural and built up area (settlement area) in Lindu enclaves is always increase every years then the water availability to support Lore Lindu National Park will decrease so the stability of rainforest becomes worst. Wetland as ecological component is important to be maintained, monitored and modeled to support the stability of rainforest margin area. Therefore, Wetland change prediction is needed to predict what will happen in the future. The objectives of this research are: 1) to identify the threatening factors affecting the wetland area in Lindu Enclave area & Lindu Lake, 2) to predict the changes of wetland area in Lindu Enclave area & Lindu Lake using Markov Cellular Automata Model. Land cover 1983 from aerial photography, Landsat TM 2002 and 2006 are used as the spatial model data. Image correction is also performed in order to acquire well ground spatial estimation. Afterward, markov chain is used to get rate value per years of wetland change area in Lindu area. Multicriteria evaluation for wetland area created with soil map, topography map, and land cover map as the variables for suitable wetland area criteria that will be used in cellular automata model. Finally, markov chain probability values for each class and multicriteria evaluation map is merged with cellular automata model with 3 x 3 cell neighbors analysis then spatial wetland change prediction map for 2021 can be created. The results of this research show that Lindu enclaves have two wetland types; natural wetland (marsh and bush land) and construction / man - made wetland (paddy field and fish pond). The changes of wetland area in Lindu enclave area for natural wetland (marsh) was - 66.01 hectares (1983-2002) with – 0.1 % annual change rate and 343.94 hectares area for human made / construction wetland area (paddy field) with 1.0 % annual change rate. The suitable area for wetland was located in every enclave area, however the most suitable area was located at the Southern part of Lindu enclave area.The prediction of spatial distribution for natural wetland area in 2021 will disappear with an annual change rate of -0.1 %. However, human made wetland such as paddy field area has increased to 1,729.06 hectares in 2021 with 0.7 % annual change rate.
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