Judul: Isolasi dan Identifikasi Bakteri Asam Laktat Dangke Asal Kabupaten Sinjai Sulawesi Selatan
Abstrak: Dangke is an Indonesian traditional food from Sinjai South Sulawesi made from buffalo and bovine milk. This product is made by clotting boiled milk with fresh fruit leaves juice of Carica papaya (papain enzyme) as coagulant. An experiment was conducted to analyze the physics characteristics of dangke, isolate and identified Lactic Acid Bacteria (LAB) from original dangke. BAL isolates were futher identified and characterized its growth at different temperature conditions and concentration of NaCl 6.5%. This experiment was arranged in a factorial completely randomized design and consisted of 3 treatments of incubation temperature (10 ºC, 37 ºC, and 45 ºC), each treatment in 3 equal replicates. Data was analyzed by analysis of variance (ANOVA) and continued to Tukey HSD. Data of bacterial growth in concentration of NaCl 6.5% was analyzed by T-test. Result identification showed that species of LAB isolated from dangke were Pediococcus pentosaceus MSS 1 and Pediococcus pentosaceus MSS 2. It is concluded that P. pentosaceus MSS 1 and P. pentosaceus MSS 2 were mesophiles LAB that could survive at 30 °C up to 45 °C incubation temperature and high salt condition with concentration of NaCl 6.5%
Keyword: P. pentosaceus MSS 2, P. pentosaceus MSS 1, LAB, dangke

Judul: Potensi Daya Hambat Bakteri Asam Laktat terhadap Cendawan pada Dangke Selama Penyimpanan
Abstrak: Dangke is a traditional dairy product in Enrengkang District, South Sulawesi. Since dangke is produced traditionally in small scale unit (home industry) the microbial contamination of dangke is critical point in food quality and safety, specifically the fungal contamination of fungi. It is necessary to develop a method to inhibit growth of fungi in dangke. Lactic acid bacteria (LAB) could be a potential biopreservative of dangke regarding to inhibition of fungal contamination. The aim of this research was to review the effectiveness of inhibition of LAB against fungi in dangke during storage. The study was devided into three steps, i.e., observation of fungal number in dangke (n=20), isolation and identification of fungi in dangke, and observation of Lactobacillus plantarum DU15 (LAB) application in dangke which was contaminated with Candida sp. The study was designed using randomized completely design with factorial pattern of 4 x 2 x 4 with 5 replications. The result showed that the average number of fungi in 20 samples of dangke from Enrekang District were 5.2 x 106 cfu/g. The main isolated fungi strains were belonged to Candida sp. (95%), Saccharomyces sp. (50%), Geotrichum sp. (35%), Rodotorula sp. (10%), Microsporum sp. (40%), Cladosporium sp. (30%), Penicillium sp. (30%), Aspergillus sp. (20%), Fusarium sp. (5%). During the storage, pH, total lactic acid, and viable count of LAB increased significantly (p<0.05). LAB could reduce fungal growth significantly (p<0.05). The number of fungi decreased from 6.72 log cfu/g to 3.99 log cfu/g after 6 days of storage
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Judul: Real Option Valuation Using Binomial Decision Trees.
Abstrak: A firm that decides to invest has a choice to invest in real assets. A method which is usually used in valuation of the project is discounted cash flow (DCF) method. But, this method can’t be used in projects that account for value of managerial flexibility, such as decision to suspend operations temporarily. Therefore, this kind of project needs to be assessed using real option. Real option valuation can be done in some ways, including the use of path-dependent simulation, as well as closed-form models like Black-Scholes model, partial differential equation, and binomial or multinomial approaches. In this paper, real option valuation using binomial decisions id discussed. Determination of the project value can be done in three steps. First, calculate the expected present value of the project. Next, calculate volatility of the project using Monte Carlo simulation. Last, construct a binomial tree to model the dynamics of the project value using the parameters of the stochastic process, and then add some decision nodes to model the project’s real options.
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