Judul: Isolasi Taurin Dari Cairan Empedu Sapi
Abstrak: Tamin atan asam 2-aminoetanasulfonat adalah asam amino bebas berwujud kristal putih pada slim kamar dengan titik leleh 300°C. Senyawa ini berperan penting dalam proses metabolisme hewan dan dimanfaatkan sebagai komponen aktif minuman berenergi, serta sebagai suplemen dalam formulasi makanan bayi. Taurin menyebar dalam setiap organ tubuh mamalia tennasuk pada empedu daIam bentuk berkonjugasi dengan asam empedu.
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Judul: EkstraksiTaurin Alami dari Ikan Gindara (Lepidocybium flavobrunneum) sebagai Crude Taurine Powder
Abstrak: Escolar (Lepidocybium flavobrunneum) is one species of deep sea fish distributed in tropical seas, one of which in Southern Ocean of Java and Sumatra. Escolar contain taurine compounds that can improve the body's physiological functions . The aim of this study are to determine the optimum temperature and time extraction to produce the product, and to determine the characteristics of the crude taurine powder from the best extraction treatment. The samples of escolar fleshs were extracted using akuades 1:1 (m/v) with different treatment of temperature (30 , 45 , 60 , 75) °C and time (30 , 45 , 60 , 75) minute. The results showed that taurine content of escolar amount of 442,01 mg/kg , but after extraction, taurine levels increased. The highest levels of taurine in the products found in the extraction treatment at 45 °C for 45 minute with the increase of taurine level concentration 224,7%. The final product is called crude taurine powder which has the characteristics such as dry powder, the color is yellow and has a savory flavor. The chemical compositions were water content 10,16%, ash content 9,44%, protein content 76,91%, and fat content 3,48%
Keyword: taurine, deep sea fish, escolar, extraction, crude taurine powder

Judul: Credit risk modeling using logistic ridge regression
Abstrak: The growth of credit of national banking may cause a greater risk faced by banks. One thing we must highlight is a way to determine whether the new applicant will be good in loan repayments. A well known and widely used method for classifying the new applicant of credit is Logistic Regression. Multicollinearity is a problem that is frequently encountered in model building. Usually, variable selection method is used for handling this problem. But sometimes it creates a new problem when the important variable does not enter to the model. Logistic Ridge Regression could be an alternative in logistic regression when multicollinearity exists. The advantage of this method is that it can handle multicollinearity without deleting any predictor variables. This research compared the performance of logistic ridge regression and logistic regression with variable selection to predict the collectability status of new applicants of credit. There were 1000 observations of German Credit data set. The 740 observations were used for modeling and 260 observations were used for validation. Backward was the best among other selection variable methods which had the highest c statistic and the model was fit by Hosmer and Lemeshow Goodness-of-Fit Test. By using backward logistic regression, it showed that among 17 variables there were eight variables which were significant in the wald test. There were many significant correlations among the predictors but the highest correlation coefficient was 0.628 which exist between duration of credit (V1) and credit amount (V2).The ridge parameter or λ was 0.001. The optimal cut point of backward logistic regression was 0.680, while for logistic ridge regression was 0.677. By comparing the c statistic and the total correctly predicted cases, we can see that the logistic ridge regression was better than backward logistic regression in training data. However, with testing data (validation), backward logistic regression was better. To have a better understanding of the model with higher correlation values between V1 and V2, V2* was generated to replace V2 and logistic regression with variable selection and ridge were also built. The result pointed out that logistic ridge regression has a little higher capability to predict the new applicant’s collectability status than logistic regression with variable selection.
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