Judul: Pemisahan Torium dan Uranium dari Pasir Monazit Pulau Bangka dengan Metode Destruksi dan Pengendapan
Abstrak: Thorium and uranium can be separated from monazite sand by destruction and precipitation method. Destruction was carried out using NaOH by wet and dry destruction method. Solids as the destruction result were diluted in concentrated HCl, followed by precipitation using NH4OH at pH 4 and 7. The precipitates were characterized by x-ray fluorescence. Thorium and uranium content from dry destruction were 2.40% and 0.137%, while those from wet destruction were 3.52% and 0.161%, respectively. Thorium and uranium from wet destruction had higher solubility than those from dry destruction method. By wet destruction methods the precipitates could not be obtained at pH 4, while at pH 7 the precipitates showed higher thorium content but lower uranium content than those by dry destruction. Based on thorium and uranium contents, the two methods did not give satification as separation output.
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Judul: Pengayaan Unsur Tanah Jarang Secara Destruksi dan Pengendapan dari Pasir Monasit Bangka
Abstrak: Rare earth elements (REEs) are lanthanide elements widely used in high-tech electronic equipment. In this study, REEs from earth minerals were enriched through several stages, namely destruction, extraction by microwave oven, and precipitation. The dry and wet destruction phases by using NaOH produced a total of 25.56% and 44.30% REEs, respectively. At the extraction by microwaves stage with HCl 37% as solvent, REEs were successfully dissolved in 30 minutes at high energy. Total amount of REEs which contained in the liquid phase from wet and dry destruction were 35.72% and 29.97%, respectively. Precipitation of REEs started when the pH value was higher than 7, reached by using NH4OH 2 N. Total precipitated REEs in wet and dry feed destruction at pH 10 was 2.76% and 1.18%, respectively. Precipitation at pH 10 gave the highest total REEs. Dry feed destruction produced precipitate that effectively enrich REEs by decreasing impurity elements, such as Si and P, and there was no Th element found. It is also contained low U element which was only 12 ppm. This is consistent with the value of percent recovery and the non-radioactive REEs which was smaller than the wet feed destruction, but contains elements of U in the amount of not less than 0.00006 g and without Th element.
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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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